{"id":1562,"date":"2025-10-16T18:21:17","date_gmt":"2025-10-16T10:21:17","guid":{"rendered":"https:\/\/qtml2025.cqt.sg\/?page_id=1562"},"modified":"2026-03-30T18:02:11","modified_gmt":"2026-03-30T10:02:11","slug":"schedule","status":"publish","type":"page","link":"https:\/\/qtml2025.cqt.sg\/schedule\/","title":{"rendered":"Schedule"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"1562\" class=\"elementor elementor-1562\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1a03989 e-con-full e-flex e-con e-parent\" data-id=\"1a03989\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t<div class=\"elementor-element elementor-element-13ab188 e-flex e-con-boxed e-con e-child\" data-id=\"13ab188\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4fdf64b elementor-widget__width-initial elementor-widget elementor-widget-heading\" data-id=\"4fdf64b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\">Schedule<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2ed95db elementor-widget elementor-widget-heading\" data-id=\"2ed95db\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\"><a href=\"https:\/\/youtube.com\/playlist?list=PL4CHL5j4Xhupf2IHRQjXDxZnXVWnCMCSK&#038;si=ODnbHQ_Df8J2jLOd\"><i>Watch recordings of keynote, invited and contributed talks here<\/i><\/a><\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-299e403 e-flex e-con-boxed e-con e-parent\" data-id=\"299e403\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6430294 elementor-widget elementor-widget-spacer\" data-id=\"6430294\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-cba3ab9 e-flex e-con-boxed e-con e-parent\" data-id=\"cba3ab9\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7b8ad07 elementor-widget elementor-widget-spacer\" data-id=\"7b8ad07\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-80b5429 e-flex e-con-boxed e-con e-parent\" data-id=\"80b5429\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-644c2a6 elementor-widget elementor-widget-eael-adv-tabs\" data-id=\"644c2a6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"eael-adv-tabs.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t        <div data-scroll-on-click=\"no\" data-scroll-speed=\"300\" id=\"eael-advance-tabs-644c2a6\" class=\"eael-advance-tabs eael-tabs-horizontal eael-tab-auto-active \" data-tabid=\"644c2a6\">\n            <div class=\"eael-tabs-nav \">\n                <ul class=\"eael-tab-inline-icon\" role=\"tablist\">\n                                            <li id=\"sunday\" class=\"inactive eael-tab-item-trigger eael-tab-nav-item\" aria-selected=\"true\" data-tab=\"1\" role=\"tab\" tabindex=\"0\" aria-controls=\"sunday-tab\" aria-expanded=\"false\">\n                            \n                                                        \n                                                            <span class=\"eael-tab-title title-after-icon\" >Sunday<\/span>                            \n                                                    <\/li>\n                                            <li id=\"monday\" class=\"inactive eael-tab-item-trigger eael-tab-nav-item\" aria-selected=\"false\" data-tab=\"2\" role=\"tab\" tabindex=\"-1\" aria-controls=\"monday-tab\" aria-expanded=\"false\">\n                            \n                                                        \n                                                            <span class=\"eael-tab-title title-after-icon\" >Monday<\/span>                            \n                                                    <\/li>\n                                            <li id=\"tuesday\" class=\"inactive eael-tab-item-trigger eael-tab-nav-item\" aria-selected=\"false\" data-tab=\"3\" role=\"tab\" tabindex=\"-1\" aria-controls=\"tuesday-tab\" aria-expanded=\"false\">\n                            \n                                                        \n                                                            <span class=\"eael-tab-title title-after-icon\" >Tuesday<\/span>                            \n                                                    <\/li>\n                                            <li id=\"wednesday\" class=\"inactive eael-tab-item-trigger eael-tab-nav-item\" aria-selected=\"false\" data-tab=\"4\" role=\"tab\" tabindex=\"-1\" aria-controls=\"wednesday-tab\" aria-expanded=\"false\">\n                            \n                                                        \n                                                            <span class=\"eael-tab-title title-after-icon\" >Wednesday<\/span>                            \n                                                    <\/li>\n                                            <li id=\"thursday\" class=\"inactive eael-tab-item-trigger eael-tab-nav-item\" aria-selected=\"false\" data-tab=\"5\" role=\"tab\" tabindex=\"-1\" aria-controls=\"thursday-tab\" aria-expanded=\"false\">\n                            \n                                                        \n                                                            <span class=\"eael-tab-title title-after-icon\" >Thursday<\/span>                            \n                                                    <\/li>\n                                            <li id=\"friday\" class=\"inactive eael-tab-item-trigger eael-tab-nav-item\" aria-selected=\"false\" data-tab=\"6\" role=\"tab\" tabindex=\"-1\" aria-controls=\"friday-tab\" aria-expanded=\"false\">\n                            \n                                                        \n                                                            <span class=\"eael-tab-title title-after-icon\" >Friday<\/span>                            \n                                                    <\/li>\n                    \n                                  <\/ul>\n            <\/div>\n            \n            <div class=\"eael-tabs-content\">\n\t\t        \n                    <div id=\"sunday-tab\" class=\"clearfix eael-tab-content-item inactive\" data-title-link=\"sunday-tab\">\n\t\t\t\t        <style>.elementor-1577 .elementor-element.elementor-element-16e2ee7{--display:flex;}.elementor-1577 .elementor-element.elementor-element-40e08ae .eael-adv-accordion .eael-accordion-list .eael-accordion-header 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svg.fa-toggle{height:16px;width:16px;line-height:16px;fill:#444;}.elementor-1577 .elementor-element.elementor-element-40e08ae .eael-adv-accordion .eael-accordion-list .eael-accordion-header .fa-toggle{color:#444;}.elementor-1577 .elementor-element.elementor-element-40e08ae .eael-adv-accordion .eael-accordion-list .eael-accordion-header .fa-toggle svg{color:#444;fill:#444;}.elementor-1577 .elementor-element.elementor-element-40e08ae .eael-adv-accordion .eael-accordion-list .eael-accordion-header.active .fa-toggle{color:#fff;}.elementor-1577 .elementor-element.elementor-element-40e08ae .eael-adv-accordion .eael-accordion-list .eael-accordion-header.active .fa-toggle svg{color:#fff;fill:#fff;}.elementor-1577 .elementor-element.elementor-element-40e08ae .eael-adv-accordion .eael-accordion-list .eael-accordion-header.active svg.fa-toggle{fill:#fff;}<\/style>\t\t<div data-elementor-type=\"container\" data-elementor-id=\"1577\" class=\"elementor elementor-1577\">\n\t\t\t\t<div class=\"elementor-element elementor-element-16e2ee7 e-flex e-con-boxed e-con e-parent\" data-id=\"16e2ee7\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-40e08ae elementor-widget elementor-widget-eael-adv-accordion\" data-id=\"40e08ae\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"eael-adv-accordion.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t            <div class=\"eael-adv-accordion\" id=\"eael-adv-accordion-40e08ae\" data-scroll-on-click=\"no\" data-scroll-speed=\"300\" data-accordion-id=\"40e08ae\" data-accordion-type=\"toggle\" data-toogle-speed=\"300\">\n            <div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"location-national-university-of-singapore\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"1\" aria-controls=\"elementor-tab-content-6801\"><span class=\"eael-advanced-accordion-icon-closed\"><svg aria-hidden=\"true\" class=\"fa-accordion-icon e-font-icon-svg e-fas-map-marker-alt\" viewBox=\"0 0 384 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M172.268 501.67C26.97 291.031 0 269.413 0 192 0 85.961 85.961 0 192 0s192 85.961 192 192c0 77.413-26.97 99.031-172.268 309.67-9.535 13.774-29.93 13.773-39.464 0zM192 272c44.183 0 80-35.817 80-80s-35.817-80-80-80-80 35.817-80 80 35.817 80 80 80z\"><\/path><\/svg><\/span><span class=\"eael-advanced-accordion-icon-opened\"><svg aria-hidden=\"true\" class=\"fa-accordion-icon e-font-icon-svg e-fas-map-marker-alt\" viewBox=\"0 0 384 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M172.268 501.67C26.97 291.031 0 269.413 0 192 0 85.961 85.961 0 192 0s192 85.961 192 192c0 77.413-26.97 99.031-172.268 309.67-9.535 13.774-29.93 13.773-39.464 0zM192 272c44.183 0 80-35.817 80-80s-35.817-80-80-80-80 35.817-80 80 35.817 80 80 80z\"><\/path><\/svg><\/span><span class=\"eael-accordion-tab-title\">Location: National University of Singapore<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-6801\" class=\"eael-accordion-content clearfix\" data-tab=\"1\" aria-labelledby=\"location-national-university-of-singapore\"><p><strong>National University of Singapore<\/strong><br \/>Lecture Theatre 31<br \/>Faculty of Science S16<br \/>6 Science Drive 2<br \/>Singapore 117546<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1030-registration\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"2\" aria-controls=\"elementor-tab-content-6802\"><span class=\"eael-accordion-tab-title\">10:30 Registration<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-6802\" class=\"eael-accordion-content clearfix\" data-tab=\"2\" aria-labelledby=\"1030-registration\"><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1100-span-stylecolor919fd1ltspanspan-stylecolorf6851ftutorialspan-testing-and-verification-for-quantum-learning-imatthias-caroi\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"3\" aria-controls=\"elementor-tab-content-6803\"><span class=\"eael-accordion-tab-title\">11:00 <span style=\"color:#919FD1\">(LT)<\/span><span style=\"color:#F6851F\">Tutorial:<\/span> Testing and Verification for Quantum Learning - <i>Matthias Caro<\/i><\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-6803\" class=\"eael-accordion-content clearfix\" data-tab=\"3\" aria-labelledby=\"1100-span-stylecolor919fd1ltspanspan-stylecolorf6851ftutorialspan-testing-and-verification-for-quantum-learning-imatthias-caroi\"><p>Abstract: In this tutorial, we will explore how techniques from property testing and interactive proofs can be leveraged in quantum learning theory. Concretely, we will consider the following questions: How can we test the assumptions of quantum learning algorithms? How can we delegate quantum learning algorithms in a verifiable manner? And how can we certify the hypotheses produced by quantum learning algorithms?<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1230-lunch\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"4\" aria-controls=\"elementor-tab-content-6804\"><span class=\"eael-accordion-tab-title\">12:30 Lunch<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-6804\" class=\"eael-accordion-content clearfix\" data-tab=\"4\" aria-labelledby=\"1230-lunch\"><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1330-span-stylecolor919fd1qaspanspan-stylecolorf6851ftutorialspan-classical-simulation-of-quantum-circuits-iarmando-angrisanii\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"5\" aria-controls=\"elementor-tab-content-6805\"><span class=\"eael-accordion-tab-title\">13:30 <span style=\"color:#919FD1\">(QA)<\/span><span style=\"color:#F6851F\">Tutorial:<\/span> Classical simulation of quantum circuits - <i>Armando Angrisani<\/i><\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-6805\" class=\"eael-accordion-content clearfix\" data-tab=\"5\" aria-labelledby=\"1330-span-stylecolor919fd1qaspanspan-stylecolorf6851ftutorialspan-classical-simulation-of-quantum-circuits-iarmando-angrisanii\"><p>Abstract: As quantum devices grow up in size while remaining affected by noise, a central question is when and how they can surpass classical methods in practice. Pauli propagation has recently emerged as a powerful classical simulation framework, significantly raising the benchmark for demonstrating quantum advantage. By approximating the evolution of quantum operators in the Pauli basis, these methods achieve rigorous performance guarantees across a wide range of noisy and noiseless circuits. In this talk, I will present recent theoretical advances in Pauli propagation and highlight its potential well beyond classical simulation, including its use in quantum-inspired machine learning models and hybrid strategies that facilitate the practical deployment of near-term quantum devices. I will also discuss how the propagation framework can be generalized from discrete-variable to continuous-variable quantum systems.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1500-coffee\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"6\" aria-controls=\"elementor-tab-content-6806\"><span class=\"eael-accordion-tab-title\">15:00 Coffee<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-6806\" class=\"eael-accordion-content clearfix\" data-tab=\"6\" aria-labelledby=\"1500-coffee\"><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1530-span-stylecolor919fd1qaspanspan-stylecolorf6851ftutorialspan-modern-techniques-in-quantum-algorithms-izane-rossii\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"7\" aria-controls=\"elementor-tab-content-6807\"><span class=\"eael-accordion-tab-title\">15:30 <span style=\"color:#919FD1\">(QA)<\/span><span style=\"color:#F6851F\">Tutorial:<\/span> Modern techniques in quantum algorithms - <i>Zane Rossi<\/i><\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-6807\" class=\"eael-accordion-content clearfix\" data-tab=\"7\" aria-labelledby=\"1530-span-stylecolor919fd1qaspanspan-stylecolorf6851ftutorialspan-modern-techniques-in-quantum-algorithms-izane-rossii\"><p>Abstract: Methods in the design and analysis of quantum algorithms have changed dramatically over the past ten years. These changes are rooted in new techniques for efficiently manipulating linear operators encoded in quantum computations: quantum signal processing (QSP) and quantum singular value transformation (QSVT). These algorithms, built from simple alternating circuit ans\u00e4tze, enable one to apply functions to the spectra of linear operators encoded as sub-blocks of unitary matrices. This ability has proven surprisingly flexible\u2014unifying, simplifying, and improving most quantum algorithms.<\/p><p>In this tutorial we review the construction and key properties of these algorithms, discuss how they are applied to solve disparate problems, and motivate recent extensions and improvements. Additionally, we highlight limitations of quantum algorithms for spectral mapping, and compare QSP\/QSVT with quantum algorithms for manipulating linear operators in other ways (e.g., linear combination of Hamiltonian simulation). We aim to keep the tutorial accessible to both quantum and classical audiences, toward clarifying open questions in the intersection of quantum algorithms, functional analysis, and numerical linear algebra.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1700-programme-ends\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"8\" aria-controls=\"elementor-tab-content-6808\"><span class=\"eael-accordion-tab-title\">17:00 Programme Ends<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-6808\" class=\"eael-accordion-content clearfix\" data-tab=\"8\" aria-labelledby=\"1700-programme-ends\"><\/div>\n\t\t\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t                    <\/div>\n\t\t        \n                    <div id=\"monday-tab\" class=\"clearfix eael-tab-content-item inactive\" data-title-link=\"monday-tab\">\n\t\t\t\t        <style>.elementor-1591 .elementor-element.elementor-element-c925bf3{--display:flex;}.elementor-1591 .elementor-element.elementor-element-b06bcb1 .eael-adv-accordion .eael-accordion-list .eael-accordion-header .eael-accordion-tab-title, \n                .elementor-1591 .elementor-element.elementor-element-b06bcb1 .eael-accordion_media-wrapper .eael-accordion_media-list .eael-accordion_media-content .eael-accordion_media-title{font-family:\"Arial\", Sans-serif;font-weight:700;}.elementor-1591 .elementor-element.elementor-element-b06bcb1 .eael-adv-accordion .eael-accordion-list .eael-accordion-header 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.elementor-element.elementor-element-b06bcb1 .eael-adv-accordion .eael-accordion-list .eael-accordion-header.active svg.fa-toggle{fill:#fff;}<\/style>\t\t<div data-elementor-type=\"container\" data-elementor-id=\"1591\" class=\"elementor elementor-1591\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c925bf3 e-flex e-con-boxed e-con e-parent\" data-id=\"c925bf3\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b06bcb1 elementor-widget elementor-widget-eael-adv-accordion\" data-id=\"b06bcb1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"eael-adv-accordion.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t            <div class=\"eael-adv-accordion\" id=\"eael-adv-accordion-b06bcb1\" data-scroll-on-click=\"no\" data-scroll-speed=\"300\" data-accordion-id=\"b06bcb1\" data-accordion-type=\"toggle\" data-toogle-speed=\"300\">\n            <div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"location-marina-bay-sands\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"1\" aria-controls=\"elementor-tab-content-1841\"><span class=\"eael-advanced-accordion-icon-closed\"><svg aria-hidden=\"true\" class=\"fa-accordion-icon e-font-icon-svg e-fas-map-marker-alt\" viewBox=\"0 0 384 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M172.268 501.67C26.97 291.031 0 269.413 0 192 0 85.961 85.961 0 192 0s192 85.961 192 192c0 77.413-26.97 99.031-172.268 309.67-9.535 13.774-29.93 13.773-39.464 0zM192 272c44.183 0 80-35.817 80-80s-35.817-80-80-80-80 35.817-80 80 35.817 80 80 80z\"><\/path><\/svg><\/span><span class=\"eael-advanced-accordion-icon-opened\"><svg aria-hidden=\"true\" class=\"fa-accordion-icon e-font-icon-svg e-fas-map-marker-alt\" viewBox=\"0 0 384 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M172.268 501.67C26.97 291.031 0 269.413 0 192 0 85.961 85.961 0 192 0s192 85.961 192 192c0 77.413-26.97 99.031-172.268 309.67-9.535 13.774-29.93 13.773-39.464 0zM192 272c44.183 0 80-35.817 80-80s-35.817-80-80-80-80 35.817-80 80 35.817 80 80 80z\"><\/path><\/svg><\/span><span class=\"eael-accordion-tab-title\">Location: Marina Bay Sands<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-1841\" class=\"eael-accordion-content clearfix\" data-tab=\"1\" aria-labelledby=\"location-marina-bay-sands\"><p><strong>Marina Bay Sands Singapore<\/strong><br \/>Sands Expo and Convention Centre,<br \/>Level 3, Heliconia Junior Ballroom<br \/>10 Bayfront Ave, Singapore 018956<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"0800-registration\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"2\" aria-controls=\"elementor-tab-content-1842\"><span class=\"eael-accordion-tab-title\">08:00 Registration<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-1842\" class=\"eael-accordion-content clearfix\" data-tab=\"2\" aria-labelledby=\"0800-registration\"><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"0900-opening-and-welcome\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"3\" aria-controls=\"elementor-tab-content-1843\"><span class=\"eael-accordion-tab-title\">09:00 Opening and welcome<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-1843\" class=\"eael-accordion-content clearfix\" data-tab=\"3\" aria-labelledby=\"0900-opening-and-welcome\"><p>Lirand\u00eb Pira &#8211; Welcome to QTML 2025<br \/><br \/>Patrick Rebentrost &#8211; Welcome to Q(T)ML<br \/><br \/>Jos\u00e9 Ignacio Latorre &#8211; Welcome to CQT and Quantum in Singapore<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"0930-span-stylecolor919fd1qaspanspan-stylecolorf6851finvited-talkspan-quantumboost-a-lazy-yet-fast-quantum-algorithm-for-learning-iamira-abbasi-\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"4\" aria-controls=\"elementor-tab-content-1844\"><span class=\"eael-accordion-tab-title\">09:30 <span style=\"color:#919FD1\">(QA)<\/span><span style=\"color:#F6851F\">Invited Talk:<\/span> QuantumBoost: A Lazy, Yet Fast, Quantum Algorithm For Learning - <i>Amira Abbas<\/i> <\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-1844\" class=\"eael-accordion-content clearfix\" data-tab=\"4\" aria-labelledby=\"0930-span-stylecolor919fd1qaspanspan-stylecolorf6851finvited-talkspan-quantumboost-a-lazy-yet-fast-quantum-algorithm-for-learning-iamira-abbasi-\"><p>Abstract: The technique of combining multiple votes to enhance the quality of a decision is the core of boosting algorithms in machine learning. In particular, boosting provably increases decision quality by combining multiple &#8220;weak learners&#8221;\u2014hypotheses that are only slightly better than random guessing\u2014into a single &#8220;strong learner&#8221; that classifies data well. Inspired by work by Barak, Hardt and Kale, I will introduce QuantumBoost, a quantum algorithm that achieves the best known runtime complexity over other boosting methods. I will also share some interesting insights in the way my collaborators (Yanlin Chen, Tuyen Nguyen and Ronald de Wolf) and I ultimately developed QuantumBoost and proved its correctness with the help of Gemini&#8217;s Deep Think model.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1030-coffee\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"5\" aria-controls=\"elementor-tab-content-1845\"><span class=\"eael-accordion-tab-title\">10:30 Coffee<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-1845\" class=\"eael-accordion-content clearfix\" data-tab=\"5\" aria-labelledby=\"1030-coffee\"><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1100-span-stylecolor919fd1physpanspan-stylecolorf6851finvited-talkspanai-for-quantum-toward-ai-enhanced-quantum-computing-applications-i-kouhei-nakajii\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"6\" aria-controls=\"elementor-tab-content-1846\"><span class=\"eael-accordion-tab-title\">11:00 <span style=\"color:#919FD1\">(PHY)<\/span><span style=\"color:#F6851F\">Invited Talk:<\/span>AI for Quantum: Toward AI-Enhanced Quantum Computing Applications - <i> Kouhei Nakaji<\/i><\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-1846\" class=\"eael-accordion-content clearfix\" data-tab=\"6\" aria-labelledby=\"1100-span-stylecolor919fd1physpanspan-stylecolorf6851finvited-talkspanai-for-quantum-toward-ai-enhanced-quantum-computing-applications-i-kouhei-nakajii\"><p>Abstract: The convergence of artificial intelligence (AI) and quantum computing represents one of the most promising frontiers in modern computational science. While &#8221;quantum for AI&#8221; has been widely explored as a potential application of quantum computing, &#8221;AI for quantum&#8221; \u2014 leveraging AI technologies to enhance quantum algorithms and quantum hardware \u2014 is now rapidly emerging, particularly with the advent of modern generative model techniques. In this talk, I will focus on how contemporary AI methods can be utilized to accelerate the development of quantum algorithms and enable next-generation quantum computing applications, including our proposed Generative Quantum Eigensolver (GQE) as a concrete example of such AI-driven approaches.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1200-span-stylecolor919fd1qaspan-quartic-quantum-speedups-for-planted-interface-\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"7\" aria-controls=\"elementor-tab-content-1847\"><span class=\"eael-accordion-tab-title\">12:00 <span style=\"color:#919FD1\">(QA)<\/span> Quartic Quantum Speedups For Planted Interface <\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-1847\" class=\"eael-accordion-content clearfix\" data-tab=\"7\" aria-labelledby=\"1200-span-stylecolor919fd1qaspan-quartic-quantum-speedups-for-planted-interface-\"><p>Authors: Alexander Schmidhuber, Ryan O&#8217;Donnell, Robin Kothari and Ryan Babbush<\/p><p>Abstract: We describe a quantum algorithm for the Planted~Noisy~k-XOR problem (also known as sparse Learning Parity with Noise) that achieves a nearly quartic (4th power) speedup over the best known classical algorithm while also only using exponentially less space. Our work generalizes and simplifies prior work of Hastings, by building on his quantum algorithm for the Tensor Principal Component Analysis (PCA) problem. We achieve our quantum speedup using a general framework based on the Kikuchi<br \/>Method (recovering the quartic speedup for Tensor PCA), and we anticipate it will yield similar speedups for other planted inference problems. These speedups rely on the fact that planted inference problems naturally instantiate the Guided Sparse Hamiltonian problem. Since planted inference problems serve as a testbed for studying the hardness of statistical learning, our work paves a path towards significant polynomial quantum speedups in machine learning.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1230-lunch\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"8\" aria-controls=\"elementor-tab-content-1848\"><span class=\"eael-accordion-tab-title\">12:30 Lunch<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-1848\" class=\"eael-accordion-content clearfix\" data-tab=\"8\" aria-labelledby=\"1230-lunch\"><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1400-span-stylecolor919fd1qaspan-productionizing-quantum-mass-production\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"9\" aria-controls=\"elementor-tab-content-1849\"><span class=\"eael-accordion-tab-title\">14:00 <span style=\"color:#919FD1\">(QA)<\/span> Productionizing Quantum Mass Production<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-1849\" class=\"eael-accordion-content clearfix\" data-tab=\"9\" aria-labelledby=\"1400-span-stylecolor919fd1qaspan-productionizing-quantum-mass-production\"><p>Authors: William J. Huggins, Tanuj Khattar and Nathan Wiebe<\/p><p>Abstract: For many practical applications of quantum computing, the most costly steps involve coherently accessing classical data. We help address this challenge by applying mass production techniques, which can reduce the cost of applying an operation multiple times in parallel. We combine these techniques with modern approaches for classical data loading based on &#8220;quantum read-only memory&#8221; (QROM). We find that we can polynomially reduce the total number of gates required for data loading, but we find no<br \/>advantage in cost models that only count the number of non-Clifford gates. Furthermore, for realistic cost models and problem sizes, we find that it is possible to reduce the cost of parallel data loading by an order of magnitude<br \/>or more. We present several applications of quantum mass production, including a scheme that uses parallel phase estimation to asymptotically reduce the gate complexity of state-of-the-art algorithms for estimating eigenvalues of<br \/>the quantum chemical Hamiltonian. We also show that mass production can be used to reduce the cost of serial calls to the same data loading oracle by precomputing several copies of a novel QROM resource state.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1420-span-stylecolor919fd1qaspan-multiple-basis-representation-of-quantum-states\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"10\" aria-controls=\"elementor-tab-content-18410\"><span class=\"eael-accordion-tab-title\">14:20 <span style=\"color:#919FD1\">(QA)<\/span> Multiple-Basis Representation Of Quantum States<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-18410\" class=\"eael-accordion-content clearfix\" data-tab=\"10\" aria-labelledby=\"1420-span-stylecolor919fd1qaspan-multiple-basis-representation-of-quantum-states\"><p>Authors: Adri\u00e1n P\u00e9rez-Salinas, Patrick Emonts, Jordi Tura Brugu\u00e9s and Vedran Dunjko<\/p><p>Abstract: Classical simulation of quantum physics is a central approach to investigating physical phenomena. Quantum computers enhance computational capabilities beyond those of classical resources, but it remains unclear to what extent existing limited quantum computers can contribute to this enhancement. In this work, we explore a new hybrid, efficient quantum-classical representation of quantum states, the multiple-basis representation.<br \/>This representation consists of a linear combination of states that are sparse in some given bases, specified by quantum circuits. Such representation is particularly appealing when considering depth-limited quantum circuits within reach of current hardware. We analyze the expressivity of multiple-basis<br \/>representation states depending on the classical simulability of their quantum circuits. In particular, we show that multiple-basis representation states include, but are not restricted to, both matrix-product states and stabilizer states. Furthermore, we investigate applications of this representation in the problems of approximation of ground states, simulation of deeper computations by specifying bases with shallow circuits, and a tomographical protocol to describe states as multiple-basis representations. We envision this work to open the path of simultaneous use of several hardware-friendly bases, a natural description of hybrid computational methods accessible for near-term<br \/>hardware.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1440-span-stylecolor919fd1qaspan-pauli-propagation-a-computational-framework-for-simulating-quantum-systems\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"11\" aria-controls=\"elementor-tab-content-18411\"><span class=\"eael-accordion-tab-title\">14:40 <span style=\"color:#919FD1\">(QA)<\/span> Pauli Propagation: A Computational Framework For Simulating Quantum Systems<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-18411\" class=\"eael-accordion-content clearfix\" data-tab=\"11\" aria-labelledby=\"1440-span-stylecolor919fd1qaspan-pauli-propagation-a-computational-framework-for-simulating-quantum-systems\"><p>Authors: Manuel Rudolph, Armando Angrisani, Tyson Jones, Yanting<br \/>Teng, Alexander Schmidhuber, Antonio Anna Mele, Marco Cerezo, Hsin-Yuang Huang and Zoe Holmes<\/p><p>Abstract: Pauli propagation (PP) is a relative newcomer to the corpus of classical simulation algorithms and yet is already competitive with other state-of-the-art methods for certain tasks. At their core, PP methods approximate the evolution of a quantum operator (typically, an observable in the Heisenberg picture) via a truncated Pauli path integral. This approach tends to be limited by very different quantum circuit characteristics compared to, for example, popular and potent tensor network methods. We provide a comprehensive account of this new simulation framework and present PauliPropagation.jl: the first general-purpose open-source Pauli propagation software. We discuss the Pauli propagation framework from high to low level, from the algorithmic formulation and known theoretical results, to our lived experience and practical implementation details. Furthermore, we present two theoretical efficiency guarantees for PP simulation of noise-free scrambling circuits and circuits subject to arbitrary local noise. We hope this new tool not only proves useful for studying quantum systems and benchmarking quantum hardware, but also inspires new classical-quantum frameworks utilizing the best and most suitable classical simulation method in conjunction with upcoming quantum<br \/>computers.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1500-span-stylecolor919fd1qaspan-quantum-hodgerank-topology-based-rank-aggregation-on-quantum-computers\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"12\" aria-controls=\"elementor-tab-content-18412\"><span class=\"eael-accordion-tab-title\">15:00 <span style=\"color:#919FD1\">(QA)<\/span> Quantum HodgeRank: Topology-Based Rank Aggregation On Quantum Computers<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-18412\" class=\"eael-accordion-content clearfix\" data-tab=\"12\" aria-labelledby=\"1500-span-stylecolor919fd1qaspan-quantum-hodgerank-topology-based-rank-aggregation-on-quantum-computers\"><p>Authors: Caesnan Leditto, Angus Southwell, Behnam Tonekaboni, Muhammad Usman and Kavan Modi<\/p><p>Abstract: HodgeRank generalizes ranking algorithms, e.g. Google PageRank, to rank alternatives based on real-world (often incomplete) data using graphs and discrete exterior calculus. It analyzes multipartite interactions on high-dimensional networks with a complexity that scales exponentially with dimension. We develop a quantum algorithm that approximates the HodgeRank solution with complexity independent of dimension. Our algorithm extracts relevant information from the state such as the ranking consistency, which achieves a superpolynomial speedup over similar classical methods.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1520-span-stylecolor919fd1qaspan-stocqs-stochastic-strategy-for-ansatz-tree-construction-in-krylov-based-linear-solver\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"13\" aria-controls=\"elementor-tab-content-18413\"><span class=\"eael-accordion-tab-title\">15:20 <span style=\"color:#919FD1\">(QA)<\/span> StoCQS: Stochastic Strategy For Ansatz Tree Construction In Krylov-Based Linear Solver<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-18413\" class=\"eael-accordion-content clearfix\" data-tab=\"13\" aria-labelledby=\"1520-span-stylecolor919fd1qaspan-stocqs-stochastic-strategy-for-ansatz-tree-construction-in-krylov-based-linear-solver\"><p>Authors: Xiufan Li, Soumik Adhikary and Patrick Rebentrost<\/p><p>Abstract: Quantum algorithms for solving linear systems of equations have seen significant development since their inception. A method uses a classical combination of quantum states (CQS) along with the so-called Ansatz tree structure to construct approximate solutions to Ax=b with provable guarantees<br \/>brought about by the Krylov subspace. However, the algorithm may require to construct the entire Ansatz tree to achieve the convergence guarantee, resulting in less efficiency when scaling to large-scale quantum machine learning. In this work, we propose StoCQS, an efficient strategy for Ansatz tree construction to solve linear systems with a convergence guarantee aided by importance sampling techniques and stochastic gradient descent, potentially with a reduced number of states. Our algorithm thus promises improved feasibility of implementing a quantum linear systems solver on large-scale quantum tasks<br \/>with provable theoretical guarantees.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1540-span-stylecolor919fd1qaspan-quantum-simulation-with-sum-of-squares-spectral-amplification\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"14\" aria-controls=\"elementor-tab-content-18414\"><span class=\"eael-accordion-tab-title\">15:40 <span style=\"color:#919FD1\">(QA)<\/span> Quantum Simulation With Sum-Of-Squares Spectral Amplification<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-18414\" class=\"eael-accordion-content clearfix\" data-tab=\"14\" aria-labelledby=\"1540-span-stylecolor919fd1qaspan-quantum-simulation-with-sum-of-squares-spectral-amplification\"><p>Authors: Nicholas Rubin, Guanghao Low, Robbie King, Eugene DePrince, Alec White, Ryan Babbush, Dominic Berry and Rolando Somma<\/p><p>Abstract: We present sum-of-squares spectral amplification (SOSSA), a framework for improving quantum simulation relevant to low-energy problems. We show how SOSSA can be applied to energy and phase estimation and provide fast quantum algorithms for these problems that significantly improve over prior art. To illustrate the power of SOSSA in applications, we consider the Sachdev-Ye-Kitaev model, a representative strongly correlated system, and demonstrate asymptotic speedups over generic simulation methods by a factor that is the square root of the system size. Our results reinforce those observed in [G.H. Low <em>etal.<\/em>, arXiv:2502.15882 (2025)], where SOSSA was used to achieve state-of-the-art gate costs for phase estimation of<br \/>real-world quantum chemistry systems.\u00a0<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1600-afternoon-tea\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"15\" aria-controls=\"elementor-tab-content-18415\"><span class=\"eael-accordion-tab-title\">16:00 Afternoon Tea<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-18415\" class=\"eael-accordion-content clearfix\" data-tab=\"15\" aria-labelledby=\"1600-afternoon-tea\"><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1630-poster-session-i\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"16\" aria-controls=\"elementor-tab-content-18416\"><span class=\"eael-accordion-tab-title\">16:30 Poster Session I<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-18416\" class=\"eael-accordion-content clearfix\" data-tab=\"16\" aria-labelledby=\"1630-poster-session-i\"><p>See the list of posters to be presented in <a href=\"\/poster-sessions#PostersI\">Session I<\/a>.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1800-programme-ends\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"17\" aria-controls=\"elementor-tab-content-18417\"><span class=\"eael-accordion-tab-title\">18:00 Programme Ends<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-18417\" class=\"eael-accordion-content clearfix\" data-tab=\"17\" aria-labelledby=\"1800-programme-ends\"><\/div>\n\t\t\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t                    <\/div>\n\t\t        \n                    <div id=\"tuesday-tab\" class=\"clearfix eael-tab-content-item inactive\" data-title-link=\"tuesday-tab\">\n\t\t\t\t        <style>.elementor-1600 .elementor-element.elementor-element-14f0a0b{--display:flex;}.elementor-1600 .elementor-element.elementor-element-4ee7f02 .eael-adv-accordion .eael-accordion-list .eael-accordion-header .eael-accordion-tab-title, \n                .elementor-1600 .elementor-element.elementor-element-4ee7f02 .eael-accordion_media-wrapper .eael-accordion_media-list .eael-accordion_media-content .eael-accordion_media-title{font-family:\"Arial\", Sans-serif;font-weight:700;}.elementor-1600 .elementor-element.elementor-element-4ee7f02 .eael-adv-accordion .eael-accordion-list .eael-accordion-header .fa-accordion-icon{font-size:16px;color:#1F1B55;}.elementor-1600 .elementor-element.elementor-element-4ee7f02 .eael-adv-accordion .eael-accordion-list .eael-accordion-header svg.fa-accordion-icon{height:16px;width:16px;line-height:16px;}.elementor-1600 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svg.fa-toggle{height:16px;width:16px;line-height:16px;fill:#444;}.elementor-1600 .elementor-element.elementor-element-4ee7f02 .eael-adv-accordion .eael-accordion-list .eael-accordion-header .fa-toggle{color:#444;}.elementor-1600 .elementor-element.elementor-element-4ee7f02 .eael-adv-accordion .eael-accordion-list .eael-accordion-header .fa-toggle svg{color:#444;fill:#444;}.elementor-1600 .elementor-element.elementor-element-4ee7f02 .eael-adv-accordion .eael-accordion-list .eael-accordion-header.active .fa-toggle{color:#fff;}.elementor-1600 .elementor-element.elementor-element-4ee7f02 .eael-adv-accordion .eael-accordion-list .eael-accordion-header.active .fa-toggle svg{color:#fff;fill:#fff;}.elementor-1600 .elementor-element.elementor-element-4ee7f02 .eael-adv-accordion .eael-accordion-list .eael-accordion-header.active svg.fa-toggle{fill:#fff;}<\/style>\t\t<div data-elementor-type=\"container\" data-elementor-id=\"1600\" class=\"elementor elementor-1600\">\n\t\t\t\t<div class=\"elementor-element elementor-element-14f0a0b e-flex e-con-boxed e-con e-parent\" data-id=\"14f0a0b\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4ee7f02 elementor-widget elementor-widget-eael-adv-accordion\" data-id=\"4ee7f02\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"eael-adv-accordion.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t            <div class=\"eael-adv-accordion\" id=\"eael-adv-accordion-4ee7f02\" data-scroll-on-click=\"no\" data-scroll-speed=\"300\" data-accordion-id=\"4ee7f02\" data-accordion-type=\"toggle\" data-toogle-speed=\"300\">\n            <div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"location-marina-bay-sands\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"1\" aria-controls=\"elementor-tab-content-8271\"><span class=\"eael-advanced-accordion-icon-closed\"><svg aria-hidden=\"true\" class=\"fa-accordion-icon e-font-icon-svg e-fas-map-marker-alt\" viewBox=\"0 0 384 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M172.268 501.67C26.97 291.031 0 269.413 0 192 0 85.961 85.961 0 192 0s192 85.961 192 192c0 77.413-26.97 99.031-172.268 309.67-9.535 13.774-29.93 13.773-39.464 0zM192 272c44.183 0 80-35.817 80-80s-35.817-80-80-80-80 35.817-80 80 35.817 80 80 80z\"><\/path><\/svg><\/span><span class=\"eael-advanced-accordion-icon-opened\"><svg aria-hidden=\"true\" class=\"fa-accordion-icon e-font-icon-svg e-fas-map-marker-alt\" viewBox=\"0 0 384 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M172.268 501.67C26.97 291.031 0 269.413 0 192 0 85.961 85.961 0 192 0s192 85.961 192 192c0 77.413-26.97 99.031-172.268 309.67-9.535 13.774-29.93 13.773-39.464 0zM192 272c44.183 0 80-35.817 80-80s-35.817-80-80-80-80 35.817-80 80 35.817 80 80 80z\"><\/path><\/svg><\/span><span class=\"eael-accordion-tab-title\">Location: Marina Bay Sands<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-8271\" class=\"eael-accordion-content clearfix\" data-tab=\"1\" aria-labelledby=\"location-marina-bay-sands\"><p><strong>Marina Bay Sands Singapore<\/strong><br \/>Sands Expo and Convention Centre,<br \/>Level 3, Heliconia Junior Ballroom<br \/>10 Bayfront Ave, Singapore 018956<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"0830-registration\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"2\" aria-controls=\"elementor-tab-content-8272\"><span class=\"eael-accordion-tab-title\">08:30 Registration<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-8272\" class=\"eael-accordion-content clearfix\" data-tab=\"2\" aria-labelledby=\"0830-registration\"><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"0900-span-stylecolor919fd1tomspanspan-stylecolorf6851fkeynotespanquantum-probe-tomography-isitan-cheni\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"3\" aria-controls=\"elementor-tab-content-8273\"><span class=\"eael-accordion-tab-title\">09:00 <span style=\"color:#919FD1\">(TOM)<\/span><span style=\"color:#F6851F\">Keynote:<\/span>Quantum Probe Tomography - <i>Sitan Chen<\/i><\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-8273\" class=\"eael-accordion-content clearfix\" data-tab=\"3\" aria-labelledby=\"0900-span-stylecolor919fd1tomspanspan-stylecolorf6851fkeynotespanquantum-probe-tomography-isitan-cheni\"><p>Abstract: Characterizing quantum many-body systems is a fundamental problem across physics, chemistry, and materials science. While significant progress has been made, many existing Hamiltonian learning protocols demand digital quantum control over the entire system, creating a disconnect from many real-world settings. Can one learn the parameters of a many-body Hamiltonian using a single local probe access to a small subsystem of a many-body thermal state undergoing time evolution? I will describe a new combination of tools from algebraic geometry and smoothed analysis that yields a provably correct algorithm for learning generic Hamiltonians in various physically natural families even in this setting. This demonstrates that robust Hamiltonian learning remains achievable even under severely constrained experimental access.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1000-span-stylecolor919fd1qaspanspan-stylecolorf6851fsubmitted-highlightspando-you-know-what-q-means\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"4\" aria-controls=\"elementor-tab-content-8274\"><span class=\"eael-accordion-tab-title\">10:00 <span style=\"color:#919FD1\">(QA)<\/span><span style=\"color:#F6851F\">Submitted highlight:<\/span>Do You Know What Q-Means?<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-8274\" class=\"eael-accordion-content clearfix\" data-tab=\"4\" aria-labelledby=\"1000-span-stylecolor919fd1qaspanspan-stylecolorf6851fsubmitted-highlightspando-you-know-what-q-means\"><p>Authors: Arjan Cornelissen, Joao F. Doriguello, Alessandro Luongo and Ewin Tang<\/p><p>Abstract: Clustering is one of the most important tools for analysis<br \/>of large datasets, and perhaps the most popular clustering<br \/>algorithm is Lloyd&#8217;s algorithm for k-means. This algorithm<br \/>takes n vectors in a d-dimensional space and outputs k<br \/>centroid vectors, which partition the vectors into clusters<br \/>based on which centroid is closest to a particular vector.<br \/>We present a classical epsilon-k-means algorithm that<br \/>performs an approximate version of one iteration of Lloyd&#8217;s<br \/>algorithm, with a time complexity that improves<br \/>exponentially in the data size n compared to previous<br \/>classical algorithms. It matches the runtime of the q-means<br \/>quantum algorithm originally proposed by Kerenidis,<br \/>Landman, Luongo, and Prakash (NeurIPS 2019). To our<br \/>knowledge, this is the fastest classical algorithm for<br \/>approximate k-means. We then turn our attention to the<br \/>quantum setting, and propose an improved version of the<br \/>q-means algorithm. Our new quantum algorithm achieves a<br \/>better runtime than previous quantum approaches and offers<br \/>a polynomial improvement over our classical epsilon-k-means<br \/>in several parameters. Unlike prior quantum algorithms, our<br \/>method does not rely on quantum linear algebra primitives.<br \/>Instead, it uses QRAM to prepare simple quantum states<br \/>based on the current cluster assignments and applies<br \/>multivariate quantum amplitude estimation. Finally, we<br \/>provide the first quantum and classical lower bounds for<br \/>performing a single iteration of the k-means problem. These<br \/>results show that our algorithms are optimal in most of the<br \/>relevant parameters.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1030-coffee\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"5\" aria-controls=\"elementor-tab-content-8275\"><span class=\"eael-accordion-tab-title\">10:30 Coffee<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-8275\" class=\"eael-accordion-content clearfix\" data-tab=\"5\" aria-labelledby=\"1030-coffee\"><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1100-span-stylecolor919fd1qnnspanon-the-cost-of-training-adversarially-robust-quantum-models\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"6\" aria-controls=\"elementor-tab-content-8276\"><span class=\"eael-accordion-tab-title\">11:00 <span style=\"color:#919FD1\">(QNN)<\/span>On The Cost Of Training Adversarially-Robust Quantum Models<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-8276\" class=\"eael-accordion-content clearfix\" data-tab=\"6\" aria-labelledby=\"1100-span-stylecolor919fd1qnnspanon-the-cost-of-training-adversarially-robust-quantum-models\"><p>Authors: Sayantan Pramanik, Chaitanya Murti, Chiranjib Bhattacharyya<br \/>and M Girish Chandra<\/p><p>Abstract: In this paper, we study the cost of training parametrised<br \/>quantum circuits (PQCs) that are employed in variational<br \/>quantum algorithms (VQAs), which depends on the number of<br \/>circuit-evaluations (CE). Since each circuit-execution is<br \/>monetarily and temporally expensive, we treat cost and CE<br \/>on an equal footing, and focus on the following questions:<\/p><p>Questions: How many CE are required to train quantum models<br \/>to an epsilon-stationary solution of the objective<br \/>function? Since the cost of estimating the gradients scales<br \/>linearly with the number of parameters, can gradient-free<br \/>algorithms be employed to get similar a performance at a<br \/>lower expense?<\/p><p>Contributions: Our contributions towards answering these<br \/>questions are threefold:<br \/>(a) As the number of CE depends linearly on the<br \/>Lipschitz-smoothness constant of the objective function, we<br \/>provide tightened bounds on it. Our result also generalises<br \/>to arbitrary loss functions besides expectation values.<br \/>(b) For a circuit with d optimisable parameters, each<br \/>(stochastic) gradient-based iteration consumes 2d<br \/>evaluations of the circuit, which hinders the scaling-up of<br \/>circuits to accommodate more parameters. We adapt a<br \/>gradient-free algorithm called the Stochastic Three Points<br \/>method to the VQA-setting, and demonstrate both<br \/>theoretically and numerically that it reduces the number of<br \/>requisite CE (over stochastic gradient descent) by a factor<br \/>of d. Particularly, in conjunction with (a), we see a<br \/>sesquilinear reduction in the cost of training PQCs &#8211; from<br \/>the order of d^3.5 to d^2.<br \/>(c) Finally, we provide theoretical and empirical evidence<br \/>to suggest that quantum models are inherently adversarially<br \/>robust. This is due to the adversarial loss being lower and<br \/>upper bounded by scalar multiples of the non-adversarial<br \/>loss. This obviates the usual (expensive) overheads of<br \/>adversarial training that arise from the estimation of<br \/>input-gradients, and augmentation of the training dataset<br \/>with adversarial examples.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1120-span-stylecolor919fd1qnnspana-pac-bayesian-approach-to-generalization-for-quantum-models\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"7\" aria-controls=\"elementor-tab-content-8277\"><span class=\"eael-accordion-tab-title\">11:20 <span style=\"color:#919FD1\">(QNN)<\/span>A PAC-Bayesian Approach To Generalization For Quantum models<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-8277\" class=\"eael-accordion-content clearfix\" data-tab=\"7\" aria-labelledby=\"1120-span-stylecolor919fd1qnnspana-pac-bayesian-approach-to-generalization-for-quantum-models\"><p>Authors: Pablo Rodriguez-Grasa, Matthias C. Caro, Jens Eisert, Elies Gil-Fuster, Franz J. Schreiber and Carlos Bravo-Prieto<\/p><p>Abstract: Generalization is a central concept in machine learning,<br \/>yet for quantum models, it is predominantly analyzed<br \/>through uniform bounds that depend on a model&#8217;s overall<br \/>capacity rather than the specific function learned. These<br \/>bounds are often too loose and insensitive to the training<br \/>process. In this work, we address this limitation by<br \/>deriving the first PAC-Bayesian generalization bound for a<br \/>broad class of quantum machine learning (QML) models. Our<br \/>framework analyzes layered circuits composed of general<br \/>quantum channels, parameterized via their process matrices,<br \/>which include unitary, dissipative, and feedforward<br \/>operations. By performing a channel perturbation analysis,<br \/>we establish a non-uniform generalization bound that<br \/>explicitly depends on the norms of the post-training<br \/>parameter matrices. This data-dependent nature allows our<br \/>bound to reflect the properties of the learned solution,<br \/>and we show that it can offer improvements over existing<br \/>uniform, covering-number-based bounds. By connecting<br \/>channel perturbation theory with the powerful PAC-Bayesian<br \/>framework, our work provides a foundational tool for a more<br \/>nuanced, training-aware analysis of generalization in QML.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1140-span-stylecolor919fd1qnnspankernal-based-dequantization-of-quantum-machine-learning\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"8\" aria-controls=\"elementor-tab-content-8278\"><span class=\"eael-accordion-tab-title\">11:40 <span style=\"color:#919FD1\">(QNN)<\/span>Kernal-based Dequantization of Quantum Machine Learning<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-8278\" class=\"eael-accordion-content clearfix\" data-tab=\"8\" aria-labelledby=\"1140-span-stylecolor919fd1qnnspankernal-based-dequantization-of-quantum-machine-learning\"><p>Authors: Alice Barthe, Jens Eisert, Bryce Fuller, Elies Gil-Fuster, Michele Grossi, Zo\u00eb Holmes, Sofi\u00e8ne Jerbi, Johannes Jakob<br \/>Meyer, Erik Recio-Armengol, Mehrad Sahebi, Seongwook Shin,<br \/>Yudai Suzuki and Ryan Sweke<\/p><p>Abstract: A key challenge in quantum machine learning (QML) is to<br \/>identify tasks where quantum models offer a genuine<br \/>advantage over classical approaches. One promising strategy<br \/>is dequantization\u2014showing that classical algorithms can<br \/>match the performance of quantum models for specific tasks.<br \/>In this work, we study classical kernel-based methods as<br \/>dequantization methods for QML models based on<br \/>parameterized quantum circuits (PQCs), including quantum<br \/>neural networks and quantum kernel methods. For a given<br \/>PQC, we define a corresponding family of trigonometric<br \/>kernels that can capture the function class expressible by<br \/>the quantum model. We approximate these kernels using<br \/>Random Fourier Features (RFF) and derive theoretical bounds<br \/>on the difference between the true risk of RFF-approximated<br \/>classical models and their quantum counterparts in both<br \/>regression and classification tasks. Based on these bounds,<br \/>we identify sufficient conditions for dequantization.<br \/>Finally, we show that in certain cases, the exact kernel<br \/>can be computed efficiently via tensor networks, removing<br \/>the need for approximation. Our results can be used in the<br \/>design of PQC-based QML models to avoid dequantization.<br \/>Moreover, our proposed dequantization schemes can be used as independent heuristic classical algorithms before<br \/>deploying costly QML models.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1200-span-stylecolor919fd1physpanquantum-circuit-simulation-with-a-local-time-dependent\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"9\" aria-controls=\"elementor-tab-content-8279\"><span class=\"eael-accordion-tab-title\">12:00 <span style=\"color:#919FD1\">(PHY+)<\/span>Quantum Circuit simulation with a local time-dependent<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-8279\" class=\"eael-accordion-content clearfix\" data-tab=\"9\" aria-labelledby=\"1200-span-stylecolor919fd1physpanquantum-circuit-simulation-with-a-local-time-dependent\"><p>Author: Maximilian Fr\u00f6hlich<\/p><p>Abstract: We introduce a new tensor network method for simulating<br \/>quantum circuits based on a locally adaptive formulation of<br \/>the Time-Dependent Variational Principle (TDVP), aimed at<br \/>overcoming limitations of the widely used Time-Evolving<br \/>Block Decimation (TEBD) algorithm. TEBD, though effective<br \/>for short-range interactions and low entanglement growth,<br \/>suffers from cumulative truncation errors and<br \/>inefficiencies when simulating long-range gates, which<br \/>require SWAP insertions that artificially increase<br \/>entanglement and computational cost.<br \/>The proposed method reformulates circuit simulation in the<br \/>Schr\u00f6dinger picture by interpreting each gate as a small<br \/>time-evolution step and projecting its generator onto the<br \/>tangent space of the MPS manifold. This local dynamic TDVP<br \/>approach enables more accurate simulations by preserving<br \/>the MPS geometry and avoiding unnecessary truncations. It<br \/>also supports dynamic bond dimension growth and direct<br \/>simulation of long-range gates without introducing SWAP<br \/>gates, thus maintaining computational efficiency.<br \/>To implement this, single-qubit gates are directly<br \/>contracted into MPS tensors, while multi-qubit gates are<br \/>handled by slicing the MPS across affected regions and<br \/>applying localized TDVP projections. A projector-splitting<br \/>formalism is used to apply only the relevant summands for<br \/>the affected qubits, significantly reducing computational<br \/>overhead. For a gate acting on q qubits, the number of<br \/>projectors is reduced from 2L\u22121 (full TDVP) to 2q\u22121,<br \/>without loss of accuracy.<br \/>Numerical benchmarks demonstrate that local dynamic TDVP<br \/>matches TEBD in accuracy while requiring significantly<br \/>fewer MPS parameters. A key example is a 36-qubit<br \/>Trotterized 1D periodic Ising circuit with long-range<br \/>interactions, where TEBD produces rapid entanglement growth<br \/>and central bond dimension blow-up, while TDVP maintains<br \/>way smoother, lower bond growth. These results suggest that<br \/>local dynamic TDVP is a powerful and scalable alternative<br \/>for simulating large quantum circuits, particularly those<br \/>involving long-range gates or requiring dynamic<br \/>entanglement handling.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1220-span-stylecolorf6851fconference-photospan\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"10\" aria-controls=\"elementor-tab-content-82710\"><span class=\"eael-accordion-tab-title\">12:20 <span style=\"color:#F6851F\">Conference Photo<\/span><\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-82710\" class=\"eael-accordion-content clearfix\" data-tab=\"10\" aria-labelledby=\"1220-span-stylecolorf6851fconference-photospan\"><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1230-lunch-\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"11\" aria-controls=\"elementor-tab-content-82711\"><span class=\"eael-accordion-tab-title\">12:30 Lunch <\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-82711\" class=\"eael-accordion-content clearfix\" data-tab=\"11\" aria-labelledby=\"1230-lunch-\"><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1400-span-stylecolor919fd1ltspanefficient-learning-for-linear-properties-of-bounded-gate-quantum-circuits\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"12\" aria-controls=\"elementor-tab-content-82712\"><span class=\"eael-accordion-tab-title\">14:00 <span style=\"color:#919FD1\">(LT)<\/span>Efficient learning for linear properties of bounded-gate quantum circuits<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-82712\" class=\"eael-accordion-content clearfix\" data-tab=\"12\" aria-labelledby=\"1400-span-stylecolor919fd1ltspanefficient-learning-for-linear-properties-of-bounded-gate-quantum-circuits\"><p>Authors: Yuxuan Du, Min-Hsiu Hsieh and Dacheng Tao<\/p><p>Abstract: The vast and complicated many-qubit state space forbids us<br \/>to comprehensively capture the dynamics of modern quantum<br \/>computers via classical simulations or quantum tomography.<br \/>Recent progress in quantum learning theory prompts a<br \/>crucial question: can linear properties of a many-qubit<br \/>circuit with d tunable RZ gates and G-d Clifford gates be<br \/>efficiently learned from measurement data generated by<br \/>varying classical inputs? In this work, we prove that the<br \/>sample complexity scaling linearly in d is required to<br \/>achieve a small prediction error, while the corresponding<br \/>computational complexity may scale exponentially in d. To<br \/>address this challenge, we propose a kernel-based method<br \/>leveraging classical shadows and truncated trigonometric<br \/>expansions, enabling a controllable trade-off between<br \/>prediction accuracy and computational overhead. Our results<br \/>advance two crucial realms in quantum computation: the<br \/>exploration of quantum algorithms with practical utilities<br \/>and learning-based quantum system certification. We conduct<br \/>numerical simulations to validate our proposals across<br \/>diverse scenarios, encompassing quantum information<br \/>processing protocols, Hamiltonian simulation, and<br \/>variational quantum algorithms up to 60 qubits.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1420-span-stylecolor919fd1ltspaninteractive-proofs-for-verifying-quantum-learning-and-testing\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"13\" aria-controls=\"elementor-tab-content-82713\"><span class=\"eael-accordion-tab-title\">14:20 <span style=\"color:#919FD1\">(LT)<\/span>Interactive proofs for verifying (quantum) learning and testing<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-82713\" class=\"eael-accordion-content clearfix\" data-tab=\"13\" aria-labelledby=\"1420-span-stylecolor919fd1ltspaninteractive-proofs-for-verifying-quantum-learning-and-testing\"><p>Authors: Matthias C. Caro, Jens Eisert, Marcel Hinsche, Marios<br \/>Ioannou, Alexander Nietner and Ryan Sweke<\/p><p>Abstract: We consider the problem of testing and learning from data<br \/>in the presence of resource constraints, such as limited<br \/>memory or weak data access, which place limitations on the<br \/>efficiency and feasibility of testing or learning. In<br \/>particular, we ask the following question: Could a<br \/>resource-constrained learner\/tester use interaction with a<br \/>resource-unconstrained but untrusted party to solve a<br \/>learning or testing problem more efficiently than they<br \/>could without such an interaction? In this work, we answer<br \/>this question both abstractly and for concrete problems, in<br \/>two complementary ways: For a wide variety of scenarios, we<br \/>prove that a resource-constrained learner cannot gain any<br \/>advantage through classical interaction with an untrusted<br \/>prover. As a special case, we show that for the vast<br \/>majority of testing and learning problems in which quantum<br \/>memory is a meaningful resource, a memory-constrained<br \/>quantum algorithm cannot overcome its limitations via<br \/>classical communication with a memory-unconstrained quantum<br \/>prover. In contrast, when quantum communication is allowed,<br \/>we construct a variety of interactive proof protocols, for<br \/>specific learning and testing problems, which allow<br \/>memory-constrained quantum verifiers to gain significant<br \/>advantages through delegation to untrusted provers. These<br \/>results highlight both the limitations and potential of<br \/>delegating learning and testing problems to resource-rich<br \/>but untrusted third parties.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1440-span-stylecolor919fd1qnnspanon-the-dynamical-lie-algebras-of-quantum-approximate-optimization-algorithms\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"14\" aria-controls=\"elementor-tab-content-82714\"><span class=\"eael-accordion-tab-title\">14:40 <span style=\"color:#919FD1\">(QNN)<\/span>On the dynamical Lie algebras of quantum approximate optimization algorithms<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-82714\" class=\"eael-accordion-content clearfix\" data-tab=\"14\" aria-labelledby=\"1440-span-stylecolor919fd1qnnspanon-the-dynamical-lie-algebras-of-quantum-approximate-optimization-algorithms\"><p>Authors: Jonathan Allcock, Miklos Santha, Pei Yuan and Shengyu Zhang<\/p><p>Abstract: Dynamical Lie algebras (DLAs) have emerged as a valuable<br \/>tool in the study of parameterized quantum circuits,<br \/>helping to characterize both their expressiveness and<br \/>trainability. In particular, the absence or presence of<br \/>barren plateaus (BPs)\u2014flat regions in parameter space<br \/>that prevent the efficient training of variational quantum<br \/>algorithms\u2014has recently been shown to be intimately<br \/>related to quantities derived from the associated DLA.<\/p><p>In this work, we investigate DLAs for the quantum<br \/>approximate optimization algorithm (QAOA), one of the<br \/>most studied variational quantum algorithms for solving<br \/>graph MaxCut and other combinatorial optimization<br \/>problems. While DLAs for QAOA circuits have been<br \/>studied before, existing results have either been based<br \/>on numerical evidence, or else correspond to DLA<br \/>generators specifically chosen to be universal for<br \/>quantum computation on a subspace of states. We<br \/>initiate an analytical study of barren plateaus and<br \/>other statistics of QAOA algorithms, and give bounds on<br \/>the dimensions of the corresponding DLAs and their<br \/>centers for general graphs. We then focus on the<br \/>$n$-vertex cycle and complete graphs. For the cycle<br \/>graph we give an explicit basis, identify its<br \/>decomposition into the direct sum of a 2-dimensional<br \/>center and a semisimple component isomorphic to n-1<br \/>copies of su(2). We give an explicit basis for this<br \/>isomorphism, and a closed-form expression for the<br \/>variance of the cost function, proving the absence of<br \/>BPs. For the complete graph we prove that the dimension<br \/>of the DLA is $O(n^3)$ and give an explicit basis for<br \/>the DLA.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1500-span-stylecolor919fd1qnnspanspan-stylecolorf6851finvited-talkspanquantum-generative-modeling-beyond-the-nisq-era-imichele-grossii\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"15\" aria-controls=\"elementor-tab-content-82715\"><span class=\"eael-accordion-tab-title\">15:00 <span style=\"color:#919FD1\">(QNN)<\/span><span style=\"color:#f6851f\">Invited talk:<\/span>Quantum Generative Modeling Beyond the NISQ Era - <i>Michele Grossi<\/i><\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-82715\" class=\"eael-accordion-content clearfix\" data-tab=\"15\" aria-labelledby=\"1500-span-stylecolor919fd1qnnspanspan-stylecolorf6851finvited-talkspanquantum-generative-modeling-beyond-the-nisq-era-imichele-grossii\"><p>Abstract: Quantum computing provides a natural framework for generative modeling through sampling tasks with established complexity-theoretic advantages, yet standard parametrized-circuit approaches face persistent challenges in trainability and scalability. This talk reports recent progress on two complementary algorithmic directions developed to address these issues. The first is a differentiable quantum generative model (DQGM) based on quantum Chebyshev transforms, which enables post-training resolution scaling and efficient sampling without additional optimization. The second centers on quantum Boltzmann machines (QBMs), which offer a fault-tolerant path to scalable generative learning. A semi-quantum RBM (sqRBM) architecture with a commuting-visible Hamiltonian structure allows closed-form expressions for probabilities and gradients, providing provable expressive advantages over classical RBMs. Building on this, a quantum variant of contrastive divergence achieves O(1) forward-pass scaling for training. Theoretical results from these works are supported by numerical simulations, outlining a scalable and resource-efficient route for quantum generative modeling beyond the NISQ era.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1600-afternoon-tea\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"16\" aria-controls=\"elementor-tab-content-82716\"><span class=\"eael-accordion-tab-title\">16:00 Afternoon Tea<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-82716\" class=\"eael-accordion-content clearfix\" data-tab=\"16\" aria-labelledby=\"1600-afternoon-tea\"><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1630-poster-session-ii\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"17\" aria-controls=\"elementor-tab-content-82717\"><span class=\"eael-accordion-tab-title\">16:30 Poster Session II<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-82717\" class=\"eael-accordion-content clearfix\" data-tab=\"17\" aria-labelledby=\"1630-poster-session-ii\"><p>See the list of posters to be presented in <a href=\"https:\/\/qtml2025.cqt.sg\/poster-sessions#PostersII\">Session II<\/a><\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1800-programme-ends\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"18\" aria-controls=\"elementor-tab-content-82718\"><span class=\"eael-accordion-tab-title\">18:00 Programme Ends<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-82718\" class=\"eael-accordion-content clearfix\" data-tab=\"18\" aria-labelledby=\"1800-programme-ends\"><\/div>\n\t\t\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t                    <\/div>\n\t\t        \n                    <div id=\"wednesday-tab\" class=\"clearfix eael-tab-content-item inactive\" data-title-link=\"wednesday-tab\">\n\t\t\t\t        <style>.elementor-1606 .elementor-element.elementor-element-c3d0b72{--display:flex;}.elementor-1606 .elementor-element.elementor-element-6f3139b .eael-adv-accordion .eael-accordion-list .eael-accordion-header .eael-accordion-tab-title, \n                .elementor-1606 .elementor-element.elementor-element-6f3139b .eael-accordion_media-wrapper .eael-accordion_media-list .eael-accordion_media-content .eael-accordion_media-title{font-family:\"Arial\", Sans-serif;font-weight:700;}.elementor-1606 .elementor-element.elementor-element-6f3139b .eael-adv-accordion .eael-accordion-list .eael-accordion-header .fa-accordion-icon{font-size:16px;color:#1F1B55;}.elementor-1606 .elementor-element.elementor-element-6f3139b .eael-adv-accordion .eael-accordion-list .eael-accordion-header svg.fa-accordion-icon{height:16px;width:16px;line-height:16px;}.elementor-1606 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svg.fa-toggle{height:16px;width:16px;line-height:16px;fill:#444;}.elementor-1606 .elementor-element.elementor-element-6f3139b .eael-adv-accordion .eael-accordion-list .eael-accordion-header .fa-toggle{color:#444;}.elementor-1606 .elementor-element.elementor-element-6f3139b .eael-adv-accordion .eael-accordion-list .eael-accordion-header .fa-toggle svg{color:#444;fill:#444;}.elementor-1606 .elementor-element.elementor-element-6f3139b .eael-adv-accordion .eael-accordion-list .eael-accordion-header.active .fa-toggle{color:#fff;}.elementor-1606 .elementor-element.elementor-element-6f3139b .eael-adv-accordion .eael-accordion-list .eael-accordion-header.active .fa-toggle svg{color:#fff;fill:#fff;}.elementor-1606 .elementor-element.elementor-element-6f3139b .eael-adv-accordion .eael-accordion-list .eael-accordion-header.active svg.fa-toggle{fill:#fff;}<\/style>\t\t<div data-elementor-type=\"container\" data-elementor-id=\"1606\" class=\"elementor elementor-1606\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c3d0b72 e-flex e-con-boxed e-con e-parent\" data-id=\"c3d0b72\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6f3139b elementor-widget elementor-widget-eael-adv-accordion\" data-id=\"6f3139b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"eael-adv-accordion.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t            <div class=\"eael-adv-accordion\" id=\"eael-adv-accordion-6f3139b\" data-scroll-on-click=\"no\" data-scroll-speed=\"300\" data-accordion-id=\"6f3139b\" data-accordion-type=\"toggle\" data-toogle-speed=\"300\">\n            <div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"location-marina-bay-sands\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"1\" aria-controls=\"elementor-tab-content-1161\"><span class=\"eael-advanced-accordion-icon-closed\"><svg aria-hidden=\"true\" class=\"fa-accordion-icon e-font-icon-svg e-fas-map-marker-alt\" viewBox=\"0 0 384 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M172.268 501.67C26.97 291.031 0 269.413 0 192 0 85.961 85.961 0 192 0s192 85.961 192 192c0 77.413-26.97 99.031-172.268 309.67-9.535 13.774-29.93 13.773-39.464 0zM192 272c44.183 0 80-35.817 80-80s-35.817-80-80-80-80 35.817-80 80 35.817 80 80 80z\"><\/path><\/svg><\/span><span class=\"eael-advanced-accordion-icon-opened\"><svg aria-hidden=\"true\" class=\"fa-accordion-icon e-font-icon-svg e-fas-map-marker-alt\" viewBox=\"0 0 384 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M172.268 501.67C26.97 291.031 0 269.413 0 192 0 85.961 85.961 0 192 0s192 85.961 192 192c0 77.413-26.97 99.031-172.268 309.67-9.535 13.774-29.93 13.773-39.464 0zM192 272c44.183 0 80-35.817 80-80s-35.817-80-80-80-80 35.817-80 80 35.817 80 80 80z\"><\/path><\/svg><\/span><span class=\"eael-accordion-tab-title\">Location: Marina Bay Sands<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-1161\" class=\"eael-accordion-content clearfix\" data-tab=\"1\" aria-labelledby=\"location-marina-bay-sands\"><p><strong>Marina Bay Sands Singapore<\/strong><\/p><p>Sands Expo and Convention Centre,<br \/>Level 3, Heliconia Junior Ballroom<br \/>10 Bayfront Ave, Singapore 018956<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"0900-span-stylecolor919fd1qnnspanspan-stylecolorf6851finvited-talkspanscalable-quantum-machine-learning-models-in-fourier-space-ijoseph-bowlesi\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"2\" aria-controls=\"elementor-tab-content-1162\"><span class=\"eael-accordion-tab-title\">09:00 <span style=\"color:#919FD1\">(QNN)<\/span><span style=\"color:#F6851F\">Invited Talk:<\/span>Scalable quantum machine learning models in Fourier space - <i>Joseph Bowles<\/i><\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-1162\" class=\"eael-accordion-content clearfix\" data-tab=\"2\" aria-labelledby=\"0900-span-stylecolor919fd1qnnspanspan-stylecolorf6851finvited-talkspanscalable-quantum-machine-learning-models-in-fourier-space-ijoseph-bowlesi\"><p>Abstract: <span>I will show how Fourier analysis can be used to construct quantum machine learning models that are massively scalable, while simultaneously encoding an important bias present in nearly all datasets. More specifically, we will see how using the quantum Fourier transform to build models in Fourier space leads to a class of universal generative machine learning models that can be trained entirely on classical hardware, enabling scaling to circuits with thousands of qubits and millions of parameters. By empirically implementing this training algorithm on large, real-world datasets, we will see how such models can be successfully trained despite the provable existence of barren plateaus under random parameter initialization. I will also introduce a new generative machine learning algorithm, called generative bandlimiting, that leverages this approach and exploits known biases in the Fourier spectra of data. Finally, I will conclude with some reflections on the overall direction of the field.<\/span><\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1000-span-stylecolor919fd1qaspanspan-stylecolorf6851fsubmitted-highlightspandecoded-quantum-interferometry\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"3\" aria-controls=\"elementor-tab-content-1163\"><span class=\"eael-accordion-tab-title\">10:00 <span style=\"color:#919FD1\">(QA)<\/span><span style=\"color:#F6851F\">Submitted Highlight:<\/span>Decoded Quantum Interferometry<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-1163\" class=\"eael-accordion-content clearfix\" data-tab=\"3\" aria-labelledby=\"1000-span-stylecolor919fd1qaspanspan-stylecolorf6851fsubmitted-highlightspandecoded-quantum-interferometry\"><p>Authors: Stephen Jordan, Noah Shutty, Mary Wootters, Adam Zalcman, Alexander Schmidhuber, Robbie King, Sergei Isakov, Tanuj Khattar and Ryan Babbush<\/p><p>Abstract: Whether quantum computers can achieve exponential speedups in optimization has been a major open question in quantum algorithms since the field began. Here we introduce a quantum algorithm called Decoded Quantum Interferometry (DQI), which uses the quantum Fourier transform to reduce optimization problems to decoding problems. For approximating optimal polynomial fits to data over finite fields, DQI efficiently achieves a better approximation ratio than any polynomial time classical algorithm known to us, thus suggesting exponential quantum speedup. Sparse unstructured optimization problems such as max-k-XORSAT are reduced to decoding of LDPC codes. We prove a theorem which allows the performance of DQI to be calculated instance-by-instance based on the empirical performance of classical decoders. We use this to construct an instance of max-XORSAT for which DQI finds an approximate optimum that cannot be found by simulated annealing or any of the other general-purpose classical heuristics that we tried, unless given five orders of magnitude more compute time than the decoding problem requires. Although we subsequently design a tailored classical solver that beats DQI within reasonable runtime, our results nevertheless demonstrate that the combination of quantum Fourier transforms with powerful decoding primitives provides a promising new approach to finding quantum speedups for hard optimization problems.<\/p><p>This submission presents the original DQI algorithm alongside recent improvements and generalizations.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1030-coffee\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"4\" aria-controls=\"elementor-tab-content-1164\"><span class=\"eael-accordion-tab-title\">10:30 Coffee<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-1164\" class=\"eael-accordion-content clearfix\" data-tab=\"4\" aria-labelledby=\"1030-coffee\"><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1100-span-stylecolor919fd1qnnspanfourier-fingerprints-of-ansatzes-in-quantum-machine-learning\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"5\" aria-controls=\"elementor-tab-content-1165\"><span class=\"eael-accordion-tab-title\">11:00 <span style=\"color:#919FD1\">(QNN)<\/span>Fourier Fingerprints of Ansatzes in Quantum Machine Learning<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-1165\" class=\"eael-accordion-content clearfix\" data-tab=\"5\" aria-labelledby=\"1100-span-stylecolor919fd1qnnspanfourier-fingerprints-of-ansatzes-in-quantum-machine-learning\">Authors: Melvin Strobl, Emre Sahin, Lucas van der Horst, Eileen\nKuehn, Achim Streit and Ben Jaderberg\n<br><br>Abstract: The training of parameterized quantum circuits (PQCs) as\nmachine learning models is one of the most widely studied\nparadigms in quantum machine learning (QML).\nIn this framework, a typical model consists of quantum\nfeature maps, which encode a classical input into the\nHilbert space, and variational ansatzes which manipulate\nthe input via trainable parameterized gates.\nImportantly, the output of these models can be represented\nas a partial Fourier series in the input, giving rise to\nthe name quantum Fourier models (QFMs).\nFully understanding how the choice of feature map and\nansatz affects the Fourier spectrum of QFMs is crucial for\nmaximizing their performance, including for popular\nframeworks such as quantum neural networks (QNNs) and\nquantum kernels.\nIn this work, we theoretically motivate the appearance of\ncorrelations between the Fourier coefficients of QFMs,\nimplying the inability to control each term in the Fourier\nseries independently.\nFor a range of popular ansatzes, we compute these\ncorrelations and find a unique pattern for each ansatz,\nwhich we call the Fourier fingerprint.\nSubsequently, we demonstrate the utility of the Fourier\ncoefficient correlation (FCC), derived from the\nfingerprint, for learning random 1D and 2D Fourier series.\nIn these experiments, we show that the FCC can effectively\npredict which ansatzes will yield the best performance.\nFinally, we demonstrate how our the FCC applies to the more\nchallenging problem of 2D jet reconstruction in high-energy\nphysics and find that the FCC can again be used to predict\nthe best performing ansatz before training occurs.\nOverall, our results illustrate that the Fourier\nfingerprint is a powerful new tool for the problem of\noptimal ansatz choice in QML.<\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1120-span-stylecolor919fd1qnnspana-unified-theory-of-quantum-neural-network-loss-landscape\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"6\" aria-controls=\"elementor-tab-content-1166\"><span class=\"eael-accordion-tab-title\">11:20 <span style=\"color:#919FD1\">(QNN)<\/span>A Unified Theory of Quantum Neural Network Loss Landscape<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-1166\" class=\"eael-accordion-content clearfix\" data-tab=\"6\" aria-labelledby=\"1120-span-stylecolor919fd1qnnspana-unified-theory-of-quantum-neural-network-loss-landscape\">Author: Eric Anschuetz\n<br><br>Abstract: Classical neural networks with random initialization\nfamously behave as Gaussian processes in the limit of many\nneurons, which allows one to completely characterize their\ntraining and generalization behavior. No such general\nunderstanding exists for quantum neural networks (QNNs),\nwhich&#8212;outside of certain special cases&#8212;are known to not\nbehave as Gaussian processes when randomly initialized. We\nhere prove that QNNs and their first two derivatives\ninstead generally form what we call Wishart processes,\nwhere certain algebraic properties of the network determine\nthe hyperparameters of the process. This Wishart process\ndescription allows us to, for the first time: give\nnecessary and sufficient conditions for a QNN architecture\nto have a Gaussian process limit; calculate the full\ngradient distribution, generalizing previously known barren\nplateau results; and calculate the local minima\ndistribution of algebraically constrained QNNs. Our unified\nframework suggests a certain simple operational definition\nfor the &#8220;&#8221;trainability&#8221;&#8221; of a given QNN model using a newly\nintroduced, experimentally accessible quantity we call the\ndegrees of freedom of the network architecture.<\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1140-span-stylecolor919fd1qnnspanvariational-quantum-algorithms-with-exact-geodesic-transport\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"7\" aria-controls=\"elementor-tab-content-1167\"><span class=\"eael-accordion-tab-title\">11:40 <span style=\"color:#919FD1\">(QNN)<\/span>Variational quantum algorithms with exact geodesic transport<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-1167\" class=\"eael-accordion-content clearfix\" data-tab=\"7\" aria-labelledby=\"1140-span-stylecolor919fd1qnnspanvariational-quantum-algorithms-with-exact-geodesic-transport\">Authors: Andr\u00e9 Ferreira-Martins, Renato M. S. Farias, Giancarlo\nCamilo, Thiago O. Maciel, Allan Tosta, Ruge Lin, Abdulla\nAlhajri, Tobias Haug and Leandro Aolita\n<br><br>Abstract: Variational quantum algorithms (VQAs) are promising\ncandidates for near-term applications of quantum computers,\nbut their training represents a major challenge in\npractice. We introduce exact-geodesic VQAs, a\nspace-curvature aware framework that enables analytic\nRiemannian optimization of variational quantum circuits\nthrough a convenient choice of circuit ansatz. Our method\nexploits the exact metric to find a near-optimal parameter\noptimization path based on exact geodesic transport with\nconjugate gradients (EGT\u2011CG). This supersedes the\ncelebrated quantum natural gradient method, in fact\nrecovering it as its first-order approximation. Further,\nthe exact-geodesic updates for our circuit ansatz have the\nsame cost as standard gradient descent. This contrasts with\nprevious metric-aware methods, which require\nresource-intensive estimations of the metric. For chemistry\nproblems of up to 14 electrons, our toolkit allows us to\nachieve up to a 20x reduction in the number of iterations\nover Adam or quantum natural gradient methods. Moreover,\nfor degenerate problems, which are notoriously difficult to\noptimize with conventional methods, we achieve rapid\nconvergence to the global minima. Our work demonstrates\nthat the cost of VQA optimization can be drastically\nreduced by harnessing the Riemannian geometry of the\nmanifold expressed by the circuit ansatz, with both\npractical and fundamental implications at the interface\nbetween quantum machine learning, differential geometry,\nand optimal control theory.<\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1200-span-stylecolor919fd1qnnspana-unifying-account-of-warm-start-guarantees-for-patches-of-quantum-landscapes\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"8\" aria-controls=\"elementor-tab-content-1168\"><span class=\"eael-accordion-tab-title\">12:00 <span style=\"color:#919FD1\">(QNN)<\/span>A unifying account of warm start guarantees for patches of quantum landscapes<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-1168\" class=\"eael-accordion-content clearfix\" data-tab=\"8\" aria-labelledby=\"1200-span-stylecolor919fd1qnnspana-unifying-account-of-warm-start-guarantees-for-patches-of-quantum-landscapes\">Authors: Hela Mhiri, Ricard Puig, Manuel Rudolph, Sacha Lerch,\nThiparat Chotibut, Supanut Thanasilp and Zoe Holmes\n<br><br>Abstract: Barren plateaus are fundamentally a statement about quantum\nloss landscapes on average but there can, and generally\nwill, exist patches of barren plateau landscapes with\nsubstantial gradients. Previous work has studied certain\nclasses of parameterized quantum circuits and found example\nregions where gradients vanish at worst polynomially in\nsystem size. Here we present a general bound that unifies\nall these previous cases and that can tackle\nphysically-motivated ans\u00e4tze that could not be analyzed\npreviously. Concretely, we analytically prove a lower-bound\non the variance of the loss that can be used to show that\nin a non-exponentially narrow region around a point with\ncurvature the loss variance cannot decay exponentially\nfast. This result is complemented by numerics and an\nupper-bound that suggest that any loss function with a\nbarren plateau will have exponentially vanishing gradients\nin any constant radius subregion. Our work thus suggests\nthat while there are hopes to be able to warm-start\nvariational quantum algorithms, any initialization strategy\nthat cannot get increasingly close to the region of\nattraction with increasing problem size is likely\ninadequate.<\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1230-lunch-span-stylecolorf6851fwomen-in-quantum-networking-lunch-span\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"9\" aria-controls=\"elementor-tab-content-1169\"><span class=\"eael-accordion-tab-title\">12:30 Lunch &amp; <span style=\"color:#F6851F\">Women in Quantum Networking Lunch <\/span><\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-1169\" class=\"eael-accordion-content clearfix\" data-tab=\"9\" aria-labelledby=\"1230-lunch-span-stylecolorf6851fwomen-in-quantum-networking-lunch-span\"><p>In addition to the conference buffet lunch, delegates today have the choice to attend a pop-up networking reception for women in quantum at the Heliconia Junior Ballroom. This event is open to all who identify as women, no registration needed.\u00a0<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1400-span-stylecolor919fd1ltspanverifiable-end-to-end-delegated-variational-quantum-algorithms\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"10\" aria-controls=\"elementor-tab-content-11610\"><span class=\"eael-accordion-tab-title\">14:00 <span style=\"color:#919FD1\">(LT)<\/span>Verifiable End-to-End Delegated Variational Quantum Algorithms<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-11610\" class=\"eael-accordion-content clearfix\" data-tab=\"10\" aria-labelledby=\"1400-span-stylecolor919fd1ltspanverifiable-end-to-end-delegated-variational-quantum-algorithms\">Authors: Matteo Antonio Inajetovic, Petros Wallden and Anna Pappa\n<br><br>Abstract: Variational quantum algorithms (VQAs) have emerged as promising candidates for solving complex optimization and machine learning tasks on near-term quantum hardware. However, due to hardware limitations, small-scale users face challenges executing quantum operations, making delegation to more powerful quantum devices desirable. In this work, we introduce a framework for delegated variational quantum algorithms (DVQAs), where a client with limited quantum capabilities delegates the execution of a VQA to a more powerful quantum server. In particular, we introduce a protocol that enables a client to delegate a variational quantum algorithm to a server while ensuring that the input, the output and also the computation itself remain secret. Additionally, if the protocol does not abort, the client can be certain that the computation outcome is indeed correct. Our approach first proposes a verifiable Protocol for delegating the quantum computation required at each optimization step of a VQA, and then combines the iterative steps into an error-resilient optimization process that offers end-to-end verifiable algorithm execution. Our results demonstrate that secure delegation of variational quantum algorithms is a realistic solution for near-term quantum networks, paving the way for practical quantum cloud computing applications.<\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1420-span-stylecolor919fd1qnnspanbayesian-quantum-orthogonal-neural-networks-for-anomaly-detection\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"11\" aria-controls=\"elementor-tab-content-11611\"><span class=\"eael-accordion-tab-title\">14:20 <span style=\"color:#919FD1\">(QNN)<\/span>Bayesian Quantum Orthogonal Neural Networks for Anomaly Detection<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-11611\" class=\"eael-accordion-content clearfix\" data-tab=\"11\" aria-labelledby=\"1420-span-stylecolor919fd1qnnspanbayesian-quantum-orthogonal-neural-networks-for-anomaly-detection\">Authors: Natansh Mathur, Brian Coyle, Nishant Jain, Snehal Raj,\nAkshat Tandon, Jasper Krauser and Rainer Stoessel\n<br><br>Abstract: Identification of defects or anomalies in 3D objects is a\ncrucial task to ensure correct functionality. In this work,\nwe combine Bayesian learning with recent developments in\nquantum and quantum-inspired machine learning, specifically\northogonal neural networks, to tackle the anomaly detection\nproblem for an industrially relevant use case. Bayesian\nlearning enables uncertainty quantification of predictions,\nwhile orthogonality in weight matrices enables smooth\ntraining. We develop orthogonal (quantum) versions of 3D\nconvolutional neural networks and show that these models\ncan successfully detect anomalies in 3D objects. To test\nthe feasibility of incorporating quantum computers into a\nquantum-enhanced anomaly detection pipeline, we perform\nhardware experiments with our models on IBM&#8217;s 127 qubit\nBrisbane device, testing the effect of noise and limited\nmeasurement shots.<\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1440-span-stylecolor919fd1qnnspannatural-gradient-and-parameter-estimation-for-quantum-boltzmann-machines\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"12\" aria-controls=\"elementor-tab-content-11612\"><span class=\"eael-accordion-tab-title\">14:40 <span style=\"color:#919FD1\">(QNN)<\/span>Natural gradient and parameter estimation for quantum Boltzmann machines<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-11612\" class=\"eael-accordion-content clearfix\" data-tab=\"12\" aria-labelledby=\"1440-span-stylecolor919fd1qnnspannatural-gradient-and-parameter-estimation-for-quantum-boltzmann-machines\">Authors: Dhrumil Patel and Mark Wilde\n<br><br>Abstract: Thermal states play a fundamental role in various areas of\nphysics, and they are becoming increasingly important in\nquantum information science, with applications related to\nsemi-definite programming, quantum Boltzmann machine\nlearning, Hamiltonian learning, and the related task of\nestimating the parameters of a Hamiltonian. Here we\nestablish formulas underlying the basic geometry of\nparameterized thermal states, and we delineate quantum\nalgorithms for estimating the values of these formulas.\nMore specifically, we prove formulas for the Fisher\u2013Bures\nand Kubo\u2013Mori information matrices of parameterized thermal\nstates, and our quantum algorithms for estimating their\nmatrix elements involve a combination of classical\nsampling, Hamiltonian simulation, and the Hadamard test.\nThese results have applications in developing a natural\ngradient descent algorithm for quantum Boltzmann machine\nlearning, which takes into account the geometry of thermal\nstates, and in establishing fundamental limitations on the\nability to estimate the parameters of a Hamiltonian, when\ngiven access to thermal-state samples. For the latter task,\nand for the special case of estimating a single parameter,\nwe sketch an algorithm that realizes a measurement that is\nasymptotically optimal for the estimation task. We finally\nstress that the natural gradient descent algorithm\ndeveloped here can be used for any machine learning problem\nthat employs the quantum Boltzmann machine ansatz.<\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1500-industry-session\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"13\" aria-controls=\"elementor-tab-content-11613\"><span class=\"eael-accordion-tab-title\">15:00 Industry Session<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-11613\" class=\"eael-accordion-content clearfix\" data-tab=\"13\" aria-labelledby=\"1500-industry-session\"><p>Opening remarks:<\/p><ul><li><strong>National Quantum Office, Singapore<\/strong> &#8211; Ling Keok Tong<\/li><\/ul><p>Speakers:<\/p><ul><li><strong>Google<\/strong>&#8211; Robbie King<\/li><li><strong>Horizon Quantum <\/strong>&#8211; Benjamin Tan<\/li><li><strong>Multiverse Computing\u00a0<\/strong>&#8211; Borja Aizpurua<\/li><li><strong>Quantinuum\u00a0<\/strong>&#8211;\u00a0Konstantinos Meichanetzidis<\/li><li><strong>QAI Ventures\u00a0<\/strong>&#8211; Angelina Frank<\/li><li><strong>Xanadu<\/strong>&#8211; Maria Schuld<\/li><\/ul><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1600-afternoon-tea\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"14\" aria-controls=\"elementor-tab-content-11614\"><span class=\"eael-accordion-tab-title\">16:00 Afternoon Tea<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-11614\" class=\"eael-accordion-content clearfix\" data-tab=\"14\" aria-labelledby=\"1600-afternoon-tea\"><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1630-span-stylecolorf6851fkeynotespanquantum-world-models-for-a-quantum-world-iseth-lloydi\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"15\" aria-controls=\"elementor-tab-content-11615\"><span class=\"eael-accordion-tab-title\">16:30 <span style=\"color:#F6851F\">Keynote:<\/span>Quantum world models for a quantum world - <i>Seth Lloyd<\/i><\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-11615\" class=\"eael-accordion-content clearfix\" data-tab=\"15\" aria-labelledby=\"1630-span-stylecolorf6851fkeynotespanquantum-world-models-for-a-quantum-world-iseth-lloydi\">Abstract: When a physical system gets information from the outside world, processes that information, and acts back on the world, it possesses a world model, which can be either implicit (biological systems) or explicit (AI\/robotics).  This talk introduces quantum world models, the models by which quantum systems interact with the quantum world.   I apply recently developed physical measures of complexity (Reilly, Lloyd 2025) to populations of quantum systems that harvest free energy from the world, and convert that energy into reproductive work.   Under the assumption that the reproductive rate of a quantum model is proportional to the amount of free energy that systems that use that model can convert into work, I prove that the population as a whole converges to an optimal quantum world model via Bayesian inference.    Quantum world models are shown to be more effective than classical models for interacting with a quantum world.<\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1830-conference-dinner-reception\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"16\" aria-controls=\"elementor-tab-content-11616\"><span class=\"eael-accordion-tab-title\">18:30 Conference Dinner Reception<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-11616\" class=\"eael-accordion-content clearfix\" data-tab=\"16\" aria-labelledby=\"1830-conference-dinner-reception\"><p>The conference dinner (which requires a ticket) will be at the Hibiscus Junior Ballroom at the conference venue.\u00a0<\/p><\/div>\n\t\t\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t                    <\/div>\n\t\t        \n                    <div id=\"thursday-tab\" class=\"clearfix eael-tab-content-item inactive\" data-title-link=\"thursday-tab\">\n\t\t\t\t        <style>.elementor-1612 .elementor-element.elementor-element-7a1328a{--display:flex;}.elementor-1612 .elementor-element.elementor-element-f5cfe53 .eael-adv-accordion .eael-accordion-list .eael-accordion-header .eael-accordion-tab-title, \n                .elementor-1612 .elementor-element.elementor-element-f5cfe53 .eael-accordion_media-wrapper .eael-accordion_media-list .eael-accordion_media-content .eael-accordion_media-title{font-family:\"Arial\", Sans-serif;font-weight:700;}.elementor-1612 .elementor-element.elementor-element-f5cfe53 .eael-adv-accordion .eael-accordion-list 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.elementor-element.elementor-element-f5cfe53 .eael-adv-accordion .eael-accordion-list .eael-accordion-header.active svg.fa-toggle{fill:#fff;}<\/style>\t\t<div data-elementor-type=\"container\" data-elementor-id=\"1612\" class=\"elementor elementor-1612\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7a1328a e-flex e-con-boxed e-con e-parent\" data-id=\"7a1328a\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f5cfe53 elementor-widget elementor-widget-eael-adv-accordion\" data-id=\"f5cfe53\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"eael-adv-accordion.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t            <div class=\"eael-adv-accordion\" id=\"eael-adv-accordion-f5cfe53\" data-scroll-on-click=\"no\" data-scroll-speed=\"300\" data-accordion-id=\"f5cfe53\" data-accordion-type=\"toggle\" data-toogle-speed=\"300\">\n            <div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"location-marina-bay-sands\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"1\" aria-controls=\"elementor-tab-content-2571\"><span class=\"eael-advanced-accordion-icon-closed\"><svg aria-hidden=\"true\" class=\"fa-accordion-icon e-font-icon-svg e-fas-map-marker-alt\" viewBox=\"0 0 384 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M172.268 501.67C26.97 291.031 0 269.413 0 192 0 85.961 85.961 0 192 0s192 85.961 192 192c0 77.413-26.97 99.031-172.268 309.67-9.535 13.774-29.93 13.773-39.464 0zM192 272c44.183 0 80-35.817 80-80s-35.817-80-80-80-80 35.817-80 80 35.817 80 80 80z\"><\/path><\/svg><\/span><span class=\"eael-advanced-accordion-icon-opened\"><svg aria-hidden=\"true\" class=\"fa-accordion-icon e-font-icon-svg e-fas-map-marker-alt\" viewBox=\"0 0 384 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M172.268 501.67C26.97 291.031 0 269.413 0 192 0 85.961 85.961 0 192 0s192 85.961 192 192c0 77.413-26.97 99.031-172.268 309.67-9.535 13.774-29.93 13.773-39.464 0zM192 272c44.183 0 80-35.817 80-80s-35.817-80-80-80-80 35.817-80 80 35.817 80 80 80z\"><\/path><\/svg><\/span><span class=\"eael-accordion-tab-title\">Location: Marina Bay Sands<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-2571\" class=\"eael-accordion-content clearfix\" data-tab=\"1\" aria-labelledby=\"location-marina-bay-sands\"><p><strong>Marina Bay Sands Singapore<\/strong><\/p><p>Sands Expo and Convention Centre,<br \/>Level 3, Heliconia Junior Ballroom<br \/>10 Bayfront Ave, Singapore 018956<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"0900-span-stylecolor919fd1ltspanspan-stylecolorf6851finvited-talkspanmodels-of-learning-for-quantum-processes-with-noise-limitations-and-adversaries-imina-doostii\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"2\" aria-controls=\"elementor-tab-content-2572\"><span class=\"eael-accordion-tab-title\">09:00 <span style=\"color:#919FD1\">(LT)<\/span><span style=\"color:#F6851F\">Invited Talk:<\/span>Models of Learning for Quantum Processes: with noise, limitations and adversaries! - <i>Mina Doosti<\/i><\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-2572\" class=\"eael-accordion-content clearfix\" data-tab=\"2\" aria-labelledby=\"0900-span-stylecolor919fd1ltspanspan-stylecolorf6851finvited-talkspanmodels-of-learning-for-quantum-processes-with-noise-limitations-and-adversaries-imina-doostii\"><p>Abstract: Characterizing a quantum system by learning its evolution is a fundamental problem with a myriad of applications. In this talk, I will explore different models for learning quantum processes from a quantum learning theory perspective, which are relevant in realistic scenarios involving noisy data or adversarial behaviour. These more-realistic quantum process learning models allow us to bridge the gap between sophisticated but contrived learning theory techniques and algorithms with provable guarantees, and real applications in areas such as physics and cryptography. I will discuss this importance and highlight two main physically motivated learning models: statistical query learning of quantum processes and agnostic process learning. Statistical queries are natural yet powerful learning models that provide both learning guarantees and robustness to noise [WD24,WD25]. Agnostic process learning [WLKD24], on the other hand, enables efficient learning of quantum processes even when the data source is noisy or potentially adversarial. Specifically, the model is formalized as follows: given query access to an unknown quantum channel \u03a6 and a known concept class C of channels, the goal is to output a quantum channel that approximates \u03a6 as well as the best channel in C, up to some error. I will also discuss several natural applications of this model, including quantum machine learning, quantum metrology, classical simulation, and error mitigation. I will present relevant techniques and conclude with a discussion of open questions and limitations in both of these quantum process learning models.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1000-span-stylecolor919fd1qaspanspan-stylecolorf6851fsubmitted-highlightspanquantum-computing-and-persistence-in-tda\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"3\" aria-controls=\"elementor-tab-content-2573\"><span class=\"eael-accordion-tab-title\">10:00 <span style=\"color:#919FD1\">(QA)<\/span><span style=\"color:#F6851F\">Submitted Highlight:<\/span>Quantum computing and persistence in TDA<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-2573\" class=\"eael-accordion-content clearfix\" data-tab=\"3\" aria-labelledby=\"1000-span-stylecolor919fd1qaspanspan-stylecolorf6851fsubmitted-highlightspanquantum-computing-and-persistence-in-tda\"><p>Authors: Casper Gyurik, Alexander Schmidhuber, Robbie King, Vedran<br \/>Dunjko and Ryu Hayakawa<\/p><p>Abstract: Topological data analysis (TDA) aims to extract<br \/>noise-robust features of a data set by studying the number<br \/>and persistence of holes in its topology. We show that a<br \/>central task of TDA &#8212; deciding whether a given hole<br \/>persists across different length scales &#8212; is<br \/>$\\mathsf{BQP}_1$-hard and contained in $\\mathsf{BQP}$,<br \/>implying an exponential quantum speedup for this task under<br \/>standard complexity-theoretic assumptions. Our results are<br \/>based on the observation that the persistence of a hole can<br \/>be encoded in the guided sparse Hamiltonian problem, where<br \/>the guiding state is constructed from a harmonic<br \/>representative of the hole.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1030-coffee\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"4\" aria-controls=\"elementor-tab-content-2574\"><span class=\"eael-accordion-tab-title\">10:30 Coffee<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-2574\" class=\"eael-accordion-content clearfix\" data-tab=\"4\" aria-labelledby=\"1030-coffee\"><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1100-span-stylecolor919fd1tomspannearly-query-optimal-classical-shadow-estimation-of-unitary-channels\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"5\" aria-controls=\"elementor-tab-content-2575\"><span class=\"eael-accordion-tab-title\">11:00 <span style=\"color:#919FD1\">(TOM)<\/span>Nearly query-optimal classical shadow estimation of unitary channels<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-2575\" class=\"eael-accordion-content clearfix\" data-tab=\"5\" aria-labelledby=\"1100-span-stylecolor919fd1tomspannearly-query-optimal-classical-shadow-estimation-of-unitary-channels\"><p>Authors: Zihao Li, Changhao Yi, You Zhou and Huangjun Zhu<\/p><p>Abstract: Classical shadow estimation (CSE) is a powerful tool for<br \/>learning the properties of quantum states and quantum<br \/>processes. Here we consider the CSE task for quantum<br \/>unitary channels. Based on collective measurements on<br \/>multiple systems, we propose a query efficient protocol for<br \/>this task, whose query complexity has a quadratic advantage<br \/>over the previous best approach for this problem, and<br \/>almost saturates the information-theoretic lower bound. To<br \/>further enhance practicality, we present a variant protocol<br \/>using only single-copy measurements, which still offers<br \/>much better query performance than previous protocols that<br \/>do not use quantum memories. This protocol can also serve<br \/>as a key subroutine for learning an arbitrary unknown<br \/>Hamiltonian from dynamics, outperforming existing<br \/>approaches to this problem.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1120-span-stylecolor919fd1tomspanshedding-light-on-classical-shadows\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"6\" aria-controls=\"elementor-tab-content-2576\"><span class=\"eael-accordion-tab-title\">11:20 <span style=\"color:#919FD1\">(TOM)<\/span>Shedding light on classical shadows<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-2576\" class=\"eael-accordion-content clearfix\" data-tab=\"6\" aria-labelledby=\"1120-span-stylecolor919fd1tomspanshedding-light-on-classical-shadows\"><p>Authors: Hugo Thomas, Pierre-Emmanuel Emeriau and Ulysse Chabaud<\/p><p>Abstract: In this work, we introduce a shadow tomography protocol<br \/>tailored to linear<br \/>optical systems. Our protocol enables the tomography of<br \/>number states\u2014and<br \/>superpositions thereof\u2014using only passive linear optical<br \/>transformations and<br \/>photon-number resolving (PNR) detectors. Extra<br \/>technicalities emerge as this<br \/>setting is not tomographic complete but is practically<br \/>relevant due to its<br \/>experimental accessibility. We adapt the classical shadow<br \/>framework to this<br \/>context and provide both sample and time complexity bounds.<br \/>In particular, we<br \/>characterize the class of observables that can be estimated<br \/>efficiently,<br \/>leveraging the visible space formalism. Our analysis<br \/>reveals that, even under<br \/>the constraints of passive linear optics, useful and<br \/>scalable shadow tomography<br \/>is possible, opening the door to new practical applications<br \/>in photonic quantum<br \/>computing.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1140-span-stylecolor919fd1tomspana-good-basis-allows-for-classical-shadows-with-arbitrary-group-representations\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"7\" aria-controls=\"elementor-tab-content-2577\"><span class=\"eael-accordion-tab-title\">11:40 <span style=\"color:#919FD1\">(TOM)<\/span>A good basis allows for classical shadows with arbitrary group representations<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-2577\" class=\"eael-accordion-content clearfix\" data-tab=\"7\" aria-labelledby=\"1140-span-stylecolor919fd1tomspana-good-basis-allows-for-classical-shadows-with-arbitrary-group-representations\"><p>Authors: Martin Larocca, Maxwell West, Marco Cerezo, Frederic<br \/>Sauvage, Roy Forestano, Nathan Killoran and David Wierichs<\/p><p>Abstract: Classical Shadows (CSs) have emerged as an efficient method<br \/>to predict many properties of an unknown quantum state. A<br \/>common denominator in CS protocols is to parametrize the<br \/>random measurement bases with a random group action. While<br \/>many different groups have been proposed, they have been<br \/>mostly studied on a case-by-case basis, like random Paulis<br \/>and Cliffords, or matchgates.<br \/>In an step toward a unified analysis, recent work showed<br \/>that one can obtain a general expression for the shadows<br \/>channel corresponding to the action of an arbitrary group<br \/>G, provided each irreducible representation appears at most<br \/>once. The importance of this realization is that it allows<br \/>one to access a whole family of CS protocols parametrized<br \/>by the choice of group representations. Moreover, the<br \/>channel can be inverted analytically, i.e., at no cost.<br \/>However, many\u2014if not most\u2014relevant settings involve<br \/>multiplicities and thus remain beyond the scope of previous<br \/>analyses.<\/p><p>In this work, we show one can go beyond this multiplicity<br \/>limitation and obtain an analytically invertible channel on<br \/>arbitrary group representations. We give an explicit choice<br \/>of basis which we call a &#8220;good basis&#8221; such that the<br \/>measurement channel becomes a weighted sum of projectors<br \/>into the irreducible representation. We complement this<br \/>analysis by deriving general bounds on the variance of the<br \/>estimators which directly relate to sample-complexity<br \/>bounds, and discuss how to implement good-basis<br \/>measurements.<\/p><p>Our method both unifies existing CS procedures based on<br \/>local, global, orthogonal, symplectic and fermionic<br \/>Gaussian unitaries, and allows us to easily generate new<br \/>protocols based on other groups, or different<br \/>representations of previous groups. For example, we<br \/>characterize novel shadow protocols based on sampling from<br \/>the orthogonal group, the spin and tensor representations<br \/>of SU(2), and the exceptional Lie group G2.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1200-span-stylecolor919fd1tomspanlearning-quantum-states-with-tunable-loss-functions\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"8\" aria-controls=\"elementor-tab-content-2578\"><span class=\"eael-accordion-tab-title\">12:00 <span style=\"color:#919FD1\">(TOM)<\/span>Learning Quantum States with Tunable Loss Functions<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-2578\" class=\"eael-accordion-content clearfix\" data-tab=\"8\" aria-labelledby=\"1200-span-stylecolor919fd1tomspanlearning-quantum-states-with-tunable-loss-functions\"><p>Authors: Yixian Qiu, Lirand\u00eb Pira and Patrick Rebentrost<\/p><p>Abstract: Learning from quantum data presents new challenges to the<br \/>paradigm of learning from data. This typically entails the<br \/>use of quantum learning models to learn quantum processes,<br \/>that come with enough subtleties to modify the theoretical<br \/>learning frameworks. This intersection warrants new<br \/>frameworks for complexity measures, including those on<br \/>quantum sample complexity and generalization bounds.<br \/>Empirical risk minimization (ERM) serves as the<br \/>foundational framework for evaluating learning models in<br \/>general. The necessity for regularization strategies leads<br \/>to the development of advanced regularization strategies<br \/>such as tilted empirical risk minimization (TERM).<br \/>Theoretical aspects of quantum learning under a quantum ERM<br \/>framework are presented in [PRX Quantum 5, 020367 (2024)].<br \/>In this work, we propose a definition for TERM suitable to<br \/>be employed when learning quantum processes, which gives<br \/>rise to quantum TERM (QTERM). By extension, QTERM can be<br \/>viewed as a regularization strategy for quantum state<br \/>learning. This work contributes to the existing literature<br \/>on quantum and classical physics threefold. First, we prove<br \/>QTERM learnability by deriving upper bounds on QTERM&#8217;s<br \/>sample complexity. Second, we establish new PAC<br \/>generalization bounds on classical TERM. Third, we present<br \/>QTERM agnostic learning guarantees for quantum hypothesis<br \/>selection. These results contribute to the broader<br \/>literature of complexity bounds on the feasibility of<br \/>learning quantum states, as well as the presence of<br \/>regularization techniques in quantum learning.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1230-lunch\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"9\" aria-controls=\"elementor-tab-content-2579\"><span class=\"eael-accordion-tab-title\">12:30 Lunch<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-2579\" class=\"eael-accordion-content clearfix\" data-tab=\"9\" aria-labelledby=\"1230-lunch\"><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1400-span-stylecolor919fd1ltspanmildly-interacting-fermionic-unitaries-are-efficiently-learnable\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"10\" aria-controls=\"elementor-tab-content-25710\"><span class=\"eael-accordion-tab-title\">14:00 <span style=\"color:#919FD1\">(LT)<\/span>Mildly-Interacting Fermionic Unitaries are Efficiently Learnable<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-25710\" class=\"eael-accordion-content clearfix\" data-tab=\"10\" aria-labelledby=\"1400-span-stylecolor919fd1ltspanmildly-interacting-fermionic-unitaries-are-efficiently-learnable\"><p>Author: Vishnu Iyer<\/p><p>Abstract: Recent work has shown that one can efficiently learn<br \/>fermionic Gaussian unitaries, also commonly known as<br \/>nearest-neighbor matchcircuits or non-interacting fermionic<br \/>unitaries. However, one could ask a similar question about<br \/>unitaries that are near Gaussian: for example, unitaries<br \/>prepared with a small number of non-Gaussian circuit<br \/>elements. These operators find significance in quantum<br \/>chemistry and many-body physics, yet no algorithm exists to<br \/>learn them.<\/p><p>We give the first such result by devising an algorithm<br \/>which makes queries to an $n$-mode fermionic unitary<br \/>$U$ prepared by at most $O(t)$ non-Gaussian gates and<br \/>returns a circuit approximating $U$ to diamond distance<br \/>$\\varepsilon$ in time $poly(n,2^t,1\/\\varepsilon)$. This<br \/>resolves a central open question of Mele and<br \/>Herasymenko under the strongest distance metric. In<br \/>fact, our algorithm is much more general: we define a<br \/>property of unitary Gaussianity known as unitary<br \/>Gaussian dimension and show that our algorithm can<br \/>learn $n$-mode unitaries of Gaussian dimension at least<br \/>$2n &#8211; O(t)$ in time $poly(n,2^t,1\/\\varepsilon)$.<br \/>Indeed, this class subsumes unitaries prepared by at<br \/>most $O(t)$ non-Gaussian gates but also includes<br \/>several unitaries that require up to $2^{O(t)}$<br \/>non-Gaussian gates to construct.<\/p><p>In addition, we give a $poly(n,1\/\\varepsilon)$-time<br \/>algorithm to distinguish whether an $n$-mode unitary is<br \/>of Gaussian dimension at least $k$ or $\\varepsilon$-far<br \/>from all such unitaries in Frobenius distance, promised<br \/>that one is the case. Along the way, we prove<br \/>structural results about near-Gaussian fermionic<br \/>unitaries that are likely to be of independent<br \/>interest.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1420-span-stylecolor919fd1ltspanlearning-pure-quantum-states-almost-without-regret\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"11\" aria-controls=\"elementor-tab-content-25711\"><span class=\"eael-accordion-tab-title\">14:20 <span style=\"color:#919FD1\">(LT)<\/span>Learning pure quantum states (almost) without regret<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-25711\" class=\"eael-accordion-content clearfix\" data-tab=\"11\" aria-labelledby=\"1420-span-stylecolor919fd1ltspanlearning-pure-quantum-states-almost-without-regret\"><p>Authors: Josep Lumbreras, Mikhail Terekhov and Marco Tomamichel<\/p><p>Abstract: We initiate the study of quantum state tomography with<br \/>minimal disturbance to the samples. Can we learn a precise<br \/>description of a quantum state through sequential<br \/>measurements of samples while at the same time making sure<br \/>that the post-measurement state of the samples is only<br \/>minimally perturbed? Defining regret as the cumulative<br \/>disturbance of all samples, the challenge is to find a<br \/>balance between the most informative sequence of<br \/>measurements on the one hand and measurements incurring<br \/>minimal regret on the other. Here we answer this question<br \/>for pure qubit states by exhibiting a protocol that<br \/>achieves maximal precision while incurring a regret that<br \/>grows only polylogarithmically with the number of samples,<br \/>a scaling that we show to be optimal.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1440-span-stylecolor919fd1ltspanhamiltonian-locality-testing-via-trotterized-postselection\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"12\" aria-controls=\"elementor-tab-content-25712\"><span class=\"eael-accordion-tab-title\">14:40 <span style=\"color:#919FD1\">(LT)<\/span>Hamiltonian Locality Testing via Trotterized Postselection<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-25712\" class=\"eael-accordion-content clearfix\" data-tab=\"12\" aria-labelledby=\"1440-span-stylecolor919fd1ltspanhamiltonian-locality-testing-via-trotterized-postselection\"><p>Authors: John Kallaugher and Daniel Liang<\/p><p>Abstract: The (tolerant) Hamiltonian locality testing problem,<br \/>introduced<br \/>in [Bluhm, Caro,Oufkir `24], is to determine whether a<br \/>Hamiltonian $H$ is $\\varepsilon_1$-close to being $k$-local<br \/>(i.e., can be written as<br \/>the sum of weight-$k$ Pauli operators) or<br \/>$\\varepsilon_2$-far from any $k$-local<br \/>Hamiltonian, given access to its time evolution operator<br \/>and using as little total<br \/>evolution time as possible, with distance typically defined<br \/>by the normalized<br \/>Frobenius norm. We give the tightest known bounds for this<br \/>problem, proving an<br \/>$O(\\sqrt{\\frac{\\eps_2}{(\\eps_2-\\eps_1)^5}})$ evolution time<br \/>upper bound and an<br \/>$\\Omega(\\frac{1}{\\eps_2-\\eps_1})$ lower bound. Our<br \/>algorithm does not require<br \/>reverse time evolution or controlled application of the<br \/>time evolution<br \/>operator, although our lower bound applies to algorithms<br \/>using either tool.<\/p><p>Furthermore, we show that if we are allowed reverse time<br \/>evolution, this<br \/>lower bound is tight, giving a matching<br \/>$O(\\frac{1}{\\eps_2-\\eps_1})$ evolution time<br \/>algorithm.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1500-span-stylecolor919fd1ltspanspan-stylecolorf6851finvited-talkspanthe-state-of-learning-stabilizer-like-states-isrinavasan-arunchalami\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"13\" aria-controls=\"elementor-tab-content-25713\"><span class=\"eael-accordion-tab-title\">15:00 <span style=\"color:#919FD1\">(LT)<\/span><span style=\"color:#F6851F\">Invited Talk:<\/span>The state of learning stabilizer-like states - <i>Srinavasan Arunchalam<\/i><\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-25713\" class=\"eael-accordion-content clearfix\" data-tab=\"13\" aria-labelledby=\"1500-span-stylecolor919fd1ltspanspan-stylecolorf6851finvited-talkspanthe-state-of-learning-stabilizer-like-states-isrinavasan-arunchalami\"><p>Abstract: This will be an overview talk wherein I go over the recent works in the last few years on learning stabilizer states (and their generalizations). I will discuss a couple of recent works wherein we give tolerant testing protocols for these states as well as applications to learning states with bounded stabilizer rank<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1600-afternoon-tea\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"14\" aria-controls=\"elementor-tab-content-25714\"><span class=\"eael-accordion-tab-title\">16:00 Afternoon Tea<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-25714\" class=\"eael-accordion-content clearfix\" data-tab=\"14\" aria-labelledby=\"1600-afternoon-tea\"><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1630-qtml-roundtable\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"15\" aria-controls=\"elementor-tab-content-25715\"><span class=\"eael-accordion-tab-title\">16:30 QTML Roundtable<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-25715\" class=\"eael-accordion-content clearfix\" data-tab=\"15\" aria-labelledby=\"1630-qtml-roundtable\"><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1700-programme-ends\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"16\" aria-controls=\"elementor-tab-content-25716\"><span class=\"eael-accordion-tab-title\">17:00 Programme Ends<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-25716\" class=\"eael-accordion-content clearfix\" data-tab=\"16\" aria-labelledby=\"1700-programme-ends\"><\/div>\n\t\t\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t                    <\/div>\n\t\t        \n                    <div id=\"friday-tab\" class=\"clearfix eael-tab-content-item inactive\" data-title-link=\"friday-tab\">\n\t\t\t\t        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.fa-toggle{color:#fff;}.elementor-1632 .elementor-element.elementor-element-3aecfec .eael-adv-accordion .eael-accordion-list .eael-accordion-header.active .fa-toggle svg{color:#fff;fill:#fff;}.elementor-1632 .elementor-element.elementor-element-3aecfec .eael-adv-accordion .eael-accordion-list .eael-accordion-header.active svg.fa-toggle{fill:#fff;}@media(min-width:768px){.elementor-1632 .elementor-element.elementor-element-68d9fa7{--width:50%;}.elementor-1632 .elementor-element.elementor-element-2873401{--width:50%;}}<\/style>\t\t<div data-elementor-type=\"container\" data-elementor-id=\"1632\" class=\"elementor elementor-1632\">\n\t\t\t\t<div class=\"elementor-element elementor-element-be2d5d5 e-flex e-con-boxed e-con e-parent\" data-id=\"be2d5d5\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-06896b2 elementor-widget elementor-widget-eael-adv-accordion\" data-id=\"06896b2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"eael-adv-accordion.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t            <div class=\"eael-adv-accordion\" id=\"eael-adv-accordion-06896b2\" data-scroll-on-click=\"no\" data-scroll-speed=\"300\" data-accordion-id=\"06896b2\" data-accordion-type=\"toggle\" data-toogle-speed=\"300\">\n            <div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"location-national-university-of-singapore\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"1\" aria-controls=\"elementor-tab-content-6851\"><span class=\"eael-advanced-accordion-icon-closed\"><svg aria-hidden=\"true\" class=\"fa-accordion-icon e-font-icon-svg e-fas-map-marker-alt\" viewBox=\"0 0 384 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M172.268 501.67C26.97 291.031 0 269.413 0 192 0 85.961 85.961 0 192 0s192 85.961 192 192c0 77.413-26.97 99.031-172.268 309.67-9.535 13.774-29.93 13.773-39.464 0zM192 272c44.183 0 80-35.817 80-80s-35.817-80-80-80-80 35.817-80 80 35.817 80 80 80z\"><\/path><\/svg><\/span><span class=\"eael-advanced-accordion-icon-opened\"><svg aria-hidden=\"true\" class=\"fa-accordion-icon e-font-icon-svg e-fas-map-marker-alt\" viewBox=\"0 0 384 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M172.268 501.67C26.97 291.031 0 269.413 0 192 0 85.961 85.961 0 192 0s192 85.961 192 192c0 77.413-26.97 99.031-172.268 309.67-9.535 13.774-29.93 13.773-39.464 0zM192 272c44.183 0 80-35.817 80-80s-35.817-80-80-80-80 35.817-80 80 35.817 80 80 80z\"><\/path><\/svg><\/span><span class=\"eael-accordion-tab-title\">Location: National University of Singapore<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-6851\" class=\"eael-accordion-content clearfix\" data-tab=\"1\" aria-labelledby=\"location-national-university-of-singapore\"><p><b>National University of Singapore<\/b><br \/><b>Lecture Theatre 16 &amp; 17<\/b><br \/>School of Computing COM2<br \/>15 Computing Drive<br \/>Singapore 117418<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"0900-span-stylecolor919fd1qnnltspanspan-stylecolorf6851finvited-talkspanshadows-of-quantum-machine-learning-and-shallow-depth-learning-separations-sofiene-jerbi\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"2\" aria-controls=\"elementor-tab-content-6852\"><span class=\"eael-accordion-tab-title\">09:00 <span style=\"color:#919FD1\">(QNN\/LT)<\/span><span style=\"color:#F6851F\">Invited Talk:<\/span>Shadows of quantum machine learning and shallow-depth learning separations, Sofiene Jerbi<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-6852\" class=\"eael-accordion-content clearfix\" data-tab=\"2\" aria-labelledby=\"0900-span-stylecolor919fd1qnnltspanspan-stylecolorf6851finvited-talkspanshadows-of-quantum-machine-learning-and-shallow-depth-learning-separations-sofiene-jerbi\"><p>Abstract: In this talk, I will present two recent works related to the question of quantum advantages in machine learning. In the first work, we address a major obstacle to the widespread use of quantum machine learning models in practice: quantum models, even once trained, still require access to a quantum computer in order to be evaluated on new data. To solve this issue, we introduce a class of quantum models where quantum resources are only required during training, while the deployment of the trained model is classical. We prove that: (i) this class of models is universal for classically-deployed quantum machine learning; (ii) it does have restricted learning capacities compared to \u2018fully quantum\u2019 models, but nonetheless (iii) it achieves a provable learning advantage over fully classical learners; where (ii) and (iii) are contingent on widely believed assumptions in complexity theory. In the second work, we refine our understanding of the regimes where quantum advantages arise in machine learning, by proving a PAC learning advantage in the realm of shallow-depth circuits. This learning advantage has the particularity that it is unconditional, meaning that we do not need to make assumptions such as the existence of classically hard, quantumly easy, cryptographic functions to show an advantage.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1000-span-stylecolor919fd1qaspanspan-stylecolorf6851fsubmitted-highlightspana-bit-of-freedom-goes-a-long-way-quantum-and-classical-algorithms-for-online-learning-of-mdps-under-a-generative-model\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"3\" aria-controls=\"elementor-tab-content-6853\"><span class=\"eael-accordion-tab-title\">10:00 <span style=\"color:#919FD1\">(QA)<\/span><span style=\"color:#F6851F\">Submitted Highlight:<\/span>A Bit of Freedom Goes a Long Way: Quantum and Classical Algorithms for Online Learning of MDPs under a Generative Model<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-6853\" class=\"eael-accordion-content clearfix\" data-tab=\"3\" aria-labelledby=\"1000-span-stylecolor919fd1qaspanspan-stylecolorf6851fsubmitted-highlightspana-bit-of-freedom-goes-a-long-way-quantum-and-classical-algorithms-for-online-learning-of-mdps-under-a-generative-model\">Authors: Andris Ambainis, Joao F. Doriguello and Debbie Huey Chih Lim\n<br><br>Abstract: We propose novel classical and quantum online algorithms\nfor learning finite-horizon and infinite-horizon\naverage-reward Markov Decision Processes (MDPs). Our\nalgorithms are based on a hybrid exploration-generative\nreinforcement learning (RL) model wherein the agent can,\nfrom time to time, freely interact with the environment in\na generative sampling fashion, i.e., by having access to a\n&#8220;simulator&#8221;. By employing known classical and new quantum\nalgorithms for approximating optimal policies under a\ngenerative model within our learning algorithms, we show\nthat it is possible to avoid several paradigms from RL like\n&#8220;optimism in the face of uncertainty&#8221; and &#8220;posterior\nsampling&#8221; and instead compute and use optimal policies\ndirectly, which yields better regret bounds compared to\nprevious works. For finite-horizon MDPs, our quantum\nalgorithm obtains regret bounds which only depend\nlogarithmically on the number of time steps $T$, thus\nbreaking the $O(\\sqrt{T})$ classical barrier. This matches\nthe time dependence of the prior quantum works of Ganguly\net al.~(arXiv&#8217;23) and Zhong et al.~(ICML&#8217;24), but with\nimproved dependence on other parameters like state space\nsize $S$ and action space size $A$. For infinite-horizon\nMDPs, our classical and quantum bounds still maintain the\n$\\widetilde{O}(\\sqrt{T})$ dependence but with better $S$\nand $A$ factors. Nonetheless, we propose a novel measure of\nregret for infinite-horizon MDPs with respect to which our\nquantum algorithm has $\\poly\\log{T}$ regret, exponentially\nbetter compared to classical algorithms. Finally, we\ngeneralise all of our results to compact continuous state\nspaces.<\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1030-coffee\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"4\" aria-controls=\"elementor-tab-content-6854\"><span class=\"eael-accordion-tab-title\">10:30 Coffee<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-6854\" class=\"eael-accordion-content clearfix\" data-tab=\"4\" aria-labelledby=\"1030-coffee\"><\/div>\n\t\t\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-0744d34 e-flex e-con-boxed e-con e-parent\" data-id=\"0744d34\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-68d9fa7 e-con-full e-flex e-con e-child\" data-id=\"68d9fa7\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-19f1071 elementor-widget elementor-widget-heading\" data-id=\"19f1071\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">Session A (PHY)<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a31e20d elementor-widget elementor-widget-eael-adv-accordion\" data-id=\"a31e20d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"eael-adv-accordion.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t            <div class=\"eael-adv-accordion\" id=\"eael-adv-accordion-a31e20d\" data-scroll-on-click=\"no\" data-scroll-speed=\"300\" data-accordion-id=\"a31e20d\" data-accordion-type=\"toggle\" data-toogle-speed=\"300\">\n            <div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1100-span-stylecolor919fd1physpanmachine-learning-derived-entanglement-witnesses-with-trainable-measurements\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"1\" aria-controls=\"elementor-tab-content-1711\"><span class=\"eael-accordion-tab-title\">11:00 <span style=\"color:#919FD1\">(PHY)<\/span>Machine Learning Derived Entanglement Witnesses with Trainable Measurements<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-1711\" class=\"eael-accordion-content clearfix\" data-tab=\"1\" aria-labelledby=\"1100-span-stylecolor919fd1physpanmachine-learning-derived-entanglement-witnesses-with-trainable-measurements\"><p>Authors: Aiden Rosebush, Alexander Greenwood and Li Qian<\/p><p>Abstract: We propose a machine learning based approach to generating<br \/>entanglement witnesses where the number of measurement<br \/>settings may be directly specified. While previous methods<br \/>of constructing witnesses express results in terms of a<br \/>standard basis for local measurements like the Pauli basis,<br \/>this algorithm directly trains and produces the specified<br \/>number of Hermitian matrices, which can be represented by<br \/>the same number of measurement settings on a given system.<br \/>For \\(N\\) qudits of dimension \\(d\\) we use the fully<br \/>separable eigenstates of the generators of \\(SU(d)\\) for<br \/>each qudit as training data to determine the correct<br \/>measurement settings, and we adjust the bias term of the<br \/>witness with a new differential program to ensure maximal<br \/>noise tolerance and perfect accuracy. Additionally, we<br \/>implemented adversarial training to produce witnesses of<br \/>higher noise tolerance, requiring even fewer measurement<br \/>settings. We provide an automated script to implement the<br \/>above process that conveniently finds witnesses with better<br \/>noise tolerance and\/or fewer measurement settings than all<br \/>existing methods in every nontrivial case we test. We apply<br \/>this method to Bell states, GHZ states, W states,<br \/>hypergraph states, and a range of qudit states. We consider<br \/>systems from 2 &#8211; 5 qubits, bipartite qudits up to d = 10,<br \/>and tripartite qutrits. Finally, we numerically verify each<br \/>of the witnesses we generated with small test sets.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1120-span-stylecolor919fd1physpanscalable-hardware-efficient-and-noise-aware-multivariate-quantum-state-preparation\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"2\" aria-controls=\"elementor-tab-content-1712\"><span class=\"eael-accordion-tab-title\">11:20 <span style=\"color:#919FD1\">(PHY)<\/span>Scalable, hardware-efficient and noise-aware multivariate quantum state preparation<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-1712\" class=\"eael-accordion-content clearfix\" data-tab=\"2\" aria-labelledby=\"1120-span-stylecolor919fd1physpanscalable-hardware-efficient-and-noise-aware-multivariate-quantum-state-preparation\"><p>Authors: Marco Ballarin, Juan Jos\u00e9 Garc\u00eda-Ripoll, David Hayes and<br \/>Michael Lubasch<\/p><p>Abstract: Quantum state preparation of high-dimensional functions is<br \/>an important component in many quantum algorithms. For<br \/>these algorithms to provide a quantum advantage, both the<br \/>classical and quantum subroutines required for state<br \/>preparation need to be as efficient as possible. Here we<br \/>present classical algorithms based on tensor network<br \/>methods that are efficient with regard to dimensionality.<br \/>To avoid the barren plateau problem during the<br \/>optimization, we devise a procedure that smoothly<br \/>transforms the circuit from an easy-to-prepare initial<br \/>function into the target multivariate function. We<br \/>illustrate the approach by numerically optimising quantum<br \/>circuits for multivariate Gaussians of sizes up to 17<br \/>dimensions and using a total of 102 qubits. Additionally,<br \/>we fine-tune the circuits composed of hardware-native<br \/>quantum gates, taking realistic experimental noise into<br \/>account, and demonstrate the experimental feasibility on<br \/>Quantinuum\u2019s H2 quantum computer, where we prepare a<br \/>9-dimensional Gaussian with polynomially decaying<br \/>correlations using 54 qubits.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1140-span-stylecolor919fd1physpanquantum-thermodynamics-semidefinite-optimization-and-quantum-boltzmann-machines\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"3\" aria-controls=\"elementor-tab-content-1713\"><span class=\"eael-accordion-tab-title\">11:40 <span style=\"color:#919FD1\">(PHY)<\/span>Quantum thermodynamics, semidefinite optimization, and quantum Boltzmann machines<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-1713\" class=\"eael-accordion-content clearfix\" data-tab=\"3\" aria-labelledby=\"1140-span-stylecolor919fd1physpanquantum-thermodynamics-semidefinite-optimization-and-quantum-boltzmann-machines\"><p>Authors: Nana Liu, Michele Minverini, Dhrumil Patel and Mark Wilde<\/p><p>Abstract: In quantum thermodynamics, a system is described by a<br \/>Hamiltonian and a list of non-commuting charges<br \/>representing conserved quantities like particle number or<br \/>electric charge, and an important goal is to determine the<br \/>system\u2019s minimum energy in the presence of these conserved<br \/>charges. Relevant quantum states of these systems are<br \/>parameterized thermal states, alternatively known as<br \/>quantum Boltzmann machines. In optimization theory, a<br \/>semi-definite program (SDP) involves a linear objective<br \/>function optimized over the cone of positive semi-definite<br \/>operators intersected with an affine space. These problems<br \/>arise from differing motivations in the physics and<br \/>optimization communities and are phrased using very<br \/>different terminology, yet they are essentially identical<br \/>mathematically. By adopting Jaynes\u2019 mindset motivated by<br \/>quantum thermodynamics, we observe that minimizing free<br \/>energy in the aforementioned thermodynamics problem,<br \/>instead of energy, leads to an elegant solution in terms of<br \/>a dual chemical potential maximization problem that is<br \/>concave in the chemical potential parameters. As such, one<br \/>can employ standard (stochastic) gradient ascent methods to<br \/>find the optimal values of these parameters, and these<br \/>methods are guaranteed to converge quickly. At low<br \/>temperature, the minimum free energy provides an excellent<br \/>approximation for the minimum energy. We then show how this<br \/>Jaynes-inspired gradient-ascent approach can be used in<br \/>both first- and second-order classical and hybrid<br \/>quantum\u2013classical algorithms for minimizing energy, and<br \/>equivalently, how it can be used for solving SDPs, with<br \/>guarantees on the runtimes of the algorithms. The hybrid<br \/>quantum\u2013classical algorithms can be considered quantum<br \/>Boltzmann machine learning algorithms for energy<br \/>minimization. The approach discussed here is well grounded<br \/>in quantum thermodynamics and, as such, provides physical<br \/>motivation underpinning why algorithms published fifty<br \/>years after Jaynes\u2019 seminal work, including the matrix<br \/>multiplicative weights update method, the matrix<br \/>exponentiated gradient update method, and their quantum<br \/>algorithmic generalizations, perform well at solving SDPs.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1200-span-stylecolor919fd1physpanquantum-state-agnostic-work-extraction-almost-without-dissipation\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"4\" aria-controls=\"elementor-tab-content-1714\"><span class=\"eael-accordion-tab-title\">12:00 <span style=\"color:#919FD1\">(PHY)<\/span>Quantum state-agnostic work extraction (almost) without dissipation<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-1714\" class=\"eael-accordion-content clearfix\" data-tab=\"4\" aria-labelledby=\"1200-span-stylecolor919fd1physpanquantum-state-agnostic-work-extraction-almost-without-dissipation\"><p>Authors: Josep Lumbreras, Ruo Cheng Huang, Yanglin Hu, Mile Gu and<br \/>Marco Tomamichel<\/p><p>Abstract: We investigate work extraction protocols designed to<br \/>transfer the maximum possible energy to a battery using<br \/>sequential access to $N$ copies of an unknown pure qubit<br \/>state. The core challenge is designing interactions to<br \/>optimally balance two competing goals: charging the battery<br \/>optimally using the qubit in hand, and acquiring more<br \/>information qubit by qubit to improve energy harvesting in<br \/>subsequent rounds. Here, we leverage the<br \/>exploration-exploitation trade-off in reinforcement<br \/>learning to develop adaptive strategies that achieve energy<br \/>dissipation that scales only polylogarithmically in $N$.<br \/>This represents an exponential improvement over current<br \/>protocols based on full state tomography.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1230-lunch\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"5\" aria-controls=\"elementor-tab-content-1715\"><span class=\"eael-accordion-tab-title\">12:30 Lunch<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-1715\" class=\"eael-accordion-content clearfix\" data-tab=\"5\" aria-labelledby=\"1230-lunch\"><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1400-span-stylecolor919fd1physpanbeyond-penrose-tensor-diagrams-with-the-zx-calculus-applications-to-quantum-computing-centerqml-condensed-matter-physics-and-quantum-gravity\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"6\" aria-controls=\"elementor-tab-content-1716\"><span class=\"eael-accordion-tab-title\">14:00 <span style=\"color:#919FD1\">(PHY+)<\/span>Beyond Penrose tensor diagrams with the ZX-calculus: Applications to quantum computing, <center>QML, condensed matter physics, and quantum gravity<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-1716\" class=\"eael-accordion-content clearfix\" data-tab=\"6\" aria-labelledby=\"1400-span-stylecolor919fd1physpanbeyond-penrose-tensor-diagrams-with-the-zx-calculus-applications-to-quantum-computing-centerqml-condensed-matter-physics-and-quantum-gravity\"><p>Authors: Richard East, Quanlong Wang, Razin Shaikh, Lia Yeh,<br \/>Boldizs\u00e1r Po\u00f3r and Bob Coecke<\/p><p>Abstract: Since its inception, the ZX calculus has provided a<br \/>diagrammatic way to reason about qubit quantum systems,<br \/>representing any quantum computation or linear map between<br \/>qubits as ZX-diagrams, a type of tensor network. It is<br \/>recognized as a universal, sound, and complete language for<br \/>qubit linear algebra. The study of quantum mechanics is<br \/>deeply integrated with the representation theory of groups<br \/>and algebras, especially SU(2) representation theory,<br \/>essential for understanding quantum properties like spin<br \/>and angular momentum. Despite advancements, a gap remained<br \/>between diagrammatic languages like the ZX calculus and the<br \/>algebraic structures in SU(2) representation theory which<br \/>are key in fields such as quantum chemistry and condensed<br \/>matter physics. The Penrose spin calculus was introduced to<br \/>bridge this gap, integrating the ZX calculus&#8217;s diagrammatic<br \/>approach with SU(2)&#8217;s algebraic depth. Inspired by<br \/>Penrose&#8217;s work on spin networks and quantum geometry, it<br \/>extends these concepts into a comprehensive diagrammatic<br \/>language for SU(2) calculations, incorporating symmetric<br \/>projectors and diagrammatic expressions of 3jm Wigner<br \/>symbols, crucial for studying spin coupling and angular<br \/>momentum. This facilitates an intuitive understanding and<br \/>manipulation of these concepts, making SU(2) concepts more<br \/>accessible and broadening their application in quantum<br \/>computing and information theory.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1420-span-stylecolor919fd1physpanscalable-neural-decoders-for-practical-real-time-quantum-error-connection\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"7\" aria-controls=\"elementor-tab-content-1717\"><span class=\"eael-accordion-tab-title\">14:20 <span style=\"color:#919FD1\">(PHY+)<\/span>Scalable Neural Decoders for Practical Real-Time Quantum Error Connection<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-1717\" class=\"eael-accordion-content clearfix\" data-tab=\"7\" aria-labelledby=\"1420-span-stylecolor919fd1physpanscalable-neural-decoders-for-practical-real-time-quantum-error-connection\"><p>Authors: Changwon Lee, Tak Hur and Daniel Kyungdeock Park<\/p><p>Abstract: Implementing efficient and scalable decoders for quantum<br \/>error correction is essential for practical quantum<br \/>computing. Recurrent transformer-based architectures such<br \/>as AlphaQubit achieve high decoding accuracy but suffer<br \/>from prohibitive computational costs. To address this, we<br \/>introduce a Mamba decoder that replaces each Multi-Head<br \/>Attention block of AlphaQubit with a Mamba module.<br \/>On Google\u2019s Sycamore memory experiment, our Mamba decoder<br \/>matches transformer-level performance, achieving logical<br \/>error rates of $2.98\\times10^{-2}$ at distance 3 and<br \/>$3.03\\times10^{-2}$ at distance 5. We further evaluate<br \/>real-time performance over 400 cycles with a<br \/>latency-dependent noise model tied to computational<br \/>complexity.<br \/>The transformer\u2019s prohibitive $O(d^4)$ complexity leads to<br \/>a severe accumulation of decoder-induced errors, whereas<br \/>the Mamba decoder\u2019s efficient $O(d^2)$ scaling avoids this<br \/>problem, demonstrating more robust performance. Our results<br \/>thus highlight Mamba&#8217;s superior speed-accuracy trade-off,<br \/>establishing it as a viable architecture for large-scale,<br \/>real-time decoders for quantum error correction.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1440-span-stylecolor919fd1physpanvariation-locc-assisted-quantum-circuits-for-long-range-entangled-states\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"8\" aria-controls=\"elementor-tab-content-1718\"><span class=\"eael-accordion-tab-title\">14:40 <span style=\"color:#919FD1\">(PHY+)<\/span>Variation LOCC-assisted quantum circuits for long-range entangled states<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-1718\" class=\"eael-accordion-content clearfix\" data-tab=\"8\" aria-labelledby=\"1440-span-stylecolor919fd1physpanvariation-locc-assisted-quantum-circuits-for-long-range-entangled-states\"><p>Authors: Yuxuan Yan, Muzhou Ma, You Zhou and Xiongfeng Ma<\/p><p>Abstract: Long-range entanglement is an important quantum resource,<br \/>particularly for topological orders and quantum error<br \/>correction. In reality, preparing long-range entangled<br \/>states requires a deep unitary circuit, which poses<br \/>significant experimental challenges. A promising avenue is<br \/>offered by replacing some quantum resources with local<br \/>operations and classical communication (LOCC). With these<br \/>classical components, one can communicate outcomes of<br \/>mid-circuit measurements in distant subsystems,<br \/>substantially reducing circuit depth in many important<br \/>cases. However, to prepare general long-range entangled<br \/>states, finding LOCC-assisted circuits of a short depth<br \/>remains an open question. Here, to address this challenge,<br \/>we propose a quantum-classical hybrid algorithm to find<br \/>optimal LOCC protocols for preparing ground states of given<br \/>Hamiltonians. In our algorithm, we introduce an e\ufb00icient<br \/>way to estimate parameter gradients and use such gradients<br \/>for variational optimization. Theoretically, we establish<br \/>the conditions for the absence of barren plateaus, ensuring<br \/>trainability at a large system size. Numerically, the<br \/>algorithm accurately solves the ground state of long-range<br \/>entangled models, such as the perturbed<br \/>Greenberger\u2013Horne\u2013Zeilinger state and surface code. Our<br \/>results demonstrate the advantage of our method over<br \/>conventional unitary variational circuits: the practical<br \/>advantage in the accuracy of estimated ground state energy<br \/>and the theoretical advantage in creating long-range<br \/>entanglement. This work has been published in Physical<br \/>Review Letters [Phys. Rev. Lett. 134, 170601 (2025)].<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1500-coffee\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"9\" aria-controls=\"elementor-tab-content-1719\"><span class=\"eael-accordion-tab-title\">15:00 Coffee<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-1719\" class=\"eael-accordion-content clearfix\" data-tab=\"9\" aria-labelledby=\"1500-coffee\"><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1530-span-stylecolor919fd1physpanauxiliary-free-replica-shadows-efficient-estimation-of-multiple-nonlinear-quantum-properties\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"10\" aria-controls=\"elementor-tab-content-17110\"><span class=\"eael-accordion-tab-title\">15:30 <span style=\"color:#919FD1\">(PHY+)<\/span>Auxiliary-Free Replica Shadows: Efficient Estimation of Multiple Nonlinear Quantum Properties<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-17110\" class=\"eael-accordion-content clearfix\" data-tab=\"10\" aria-labelledby=\"1530-span-stylecolor919fd1physpanauxiliary-free-replica-shadows-efficient-estimation-of-multiple-nonlinear-quantum-properties\"><p>Authors: Qing Liu, Zihao Li, Xiao Yuan, Huangjun Zhu and You Zhou<\/p><p>Abstract: Efficiently measuring nonlinear properties is a significant<br \/>yet challenging task from quantum information processing to<br \/>many-body physics. Current methodologies often suffer from<br \/>an exponential sampling cost or require auxiliary qubits<br \/>and deep quantum circuits. To address these limitations, we<br \/>propose an efficient auxiliary-free replica shadow (AFRS)<br \/>framework, which leverages the power of the joint<br \/>entangling operation on a few input replicas while<br \/>integrating the mindset of shadow estimation. We rigorously<br \/>prove that AFRS can offer exponential improvements in<br \/>estimation accuracy compared with the conventional shadow<br \/>method, and facilitate the simultaneous estimation of<br \/>various nonlinear properties, unlike the destructive swap<br \/>test. Additionally, we introduce an advanced local-AFRS<br \/>variant tailored to estimating local observables with<br \/>constant-depth quantum circuits, significantly simplifying<br \/>the experimental implementation. Our work paves the way for<br \/>efficient and practical quantum measurements on near-term<br \/>quantum hardware.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1550-span-stylecolor919fd1physpanoptimal-haar-random-fermionic-linear-optics-circuits\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"11\" aria-controls=\"elementor-tab-content-17111\"><span class=\"eael-accordion-tab-title\">15:50 <span style=\"color:#919FD1\">(PHY+)<\/span>Optimal Haar random fermionic linear optics circuits<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-17111\" class=\"eael-accordion-content clearfix\" data-tab=\"11\" aria-labelledby=\"1550-span-stylecolor919fd1physpanoptimal-haar-random-fermionic-linear-optics-circuits\"><p>Authors: Paolo Braccia, N. L. Diaz, Martin Larocca, M. Cerezo and Diego Garc\u00eda-Mart\u00edn<\/p><p>Abstract: Sampling unitary Fermionic Linear Optics (FLO), or<br \/>matchgate circuits, has become a fundamental tool in<br \/>quantum information. Such capability enables a large number<br \/>of applications ranging from randomized benchmarking of<br \/>continuous gate sets, to fermionic classical shadows. In<br \/>this work, we introduce optimal algorithms to sample over<br \/>the non-particle-preserving (active) and<br \/>particle-preserving (passive) FLO Haar measures. In<br \/>particular, we provide appropriate distributions for the<br \/>gates of $n$-qubit parametrized circuits which produce<br \/>random active and passive FLO. In contrast to previous<br \/>approaches, which either incur classical<br \/>$\\mathcal{O}(n^3)$ compilation costs or have suboptimal<br \/>depths, our methods directly output circuits which<br \/>\\textit{simultaneously} achieve an optimal<br \/>down-to-the-constant-factor $\\Theta(n)$ depth and<br \/>$\\Theta(n^2)$ gate count; with only a $\\Theta(n^2)$<br \/>classical overhead. Finally, we also provide quantum<br \/>circuits to sample Clifford FLO with an optimal<br \/>$\\Theta(n^2)$ gate count.<\/p><\/div>\n\t\t\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-2873401 e-con-full e-flex e-con e-child\" data-id=\"2873401\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4356463 elementor-widget elementor-widget-heading\" data-id=\"4356463\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">Session B (QNN + LT)<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f151bad elementor-widget elementor-widget-eael-adv-accordion\" data-id=\"f151bad\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"eael-adv-accordion.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t            <div class=\"eael-adv-accordion\" id=\"eael-adv-accordion-f151bad\" data-scroll-on-click=\"no\" data-scroll-speed=\"300\" data-accordion-id=\"f151bad\" data-accordion-type=\"toggle\" data-toogle-speed=\"300\">\n            <div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1100-span-stylecolor919fd1qnnspanefficient-quantum-enhanced-classical-simulation-for-patches-of-quantum-landscapes\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"1\" aria-controls=\"elementor-tab-content-2531\"><span class=\"eael-accordion-tab-title\">11:00 <span style=\"color:#919FD1\">(QNN)<\/span>Efficient quantum-enhanced classical simulation for patches of quantum landscapes<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-2531\" class=\"eael-accordion-content clearfix\" data-tab=\"1\" aria-labelledby=\"1100-span-stylecolor919fd1qnnspanefficient-quantum-enhanced-classical-simulation-for-patches-of-quantum-landscapes\"><p>Authors: Sacha Lerch, Ricard Puig, Manuel Rudolph, Armando<br \/>Angrisani, Tyson Jones, Supanut Thanasilp, Marco Cerezo and<br \/>Zoe Holmes<\/p><p>Abstract: Understanding the capabilities of classical simulation<br \/>methods is key to identifying where quantum computers are<br \/>advantageous. Not only does this ensure that quantum<br \/>computers are used only where necessary, but also one can<br \/>potentially identify subroutines that can be offloaded onto<br \/>a classical device. In this work, we show that it is always<br \/>possible to generate a classical surrogate of a sub-region<br \/>(dubbed a \u201cpatch\u201d) of an expectation landscape produced by<br \/>a parameterized quantum circuit. That is, we provide a<br \/>quantum-enhanced classical algorithm which, after simple<br \/>measurements on a quantum device, allows one to classically<br \/>simulate approximate expectation values of a subregion of a<br \/>landscape. We provide time and sample complexity guarantees<br \/>for a range of families of circuits of interest, and<br \/>further numerically demonstrate our simulation algorithms<br \/>on an exactly verifiable simulation of a Hamiltonian<br \/>variational ansatz and long-time dynamics simulation on a<br \/>127-qubit heavy-hex topology.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1120-span-stylecolor919fd1qnnspanpolynomial-speed-up-in-photonic-neural-networks-via-adaptive-state-injection\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"2\" aria-controls=\"elementor-tab-content-2532\"><span class=\"eael-accordion-tab-title\">11:20 <span style=\"color:#919FD1\">(QNN)<\/span>Polynomial Speed-Up in Photonic Neural Networks via Adaptive State Injection<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-2532\" class=\"eael-accordion-content clearfix\" data-tab=\"2\" aria-labelledby=\"1120-span-stylecolor919fd1qnnspanpolynomial-speed-up-in-photonic-neural-networks-via-adaptive-state-injection\"><p>Authors: L\u00e9o Monbroussou, Beatrice Polacchi, Verena Yacoub, Eliott<br \/>Z. Mamon, Hugo Thomas, Eugenio Caruccio, Giovanni Rodari,<br \/>Francesco Hoch, Gonzalo Carvacho, Nicolo Spagnolo, Taira<br \/>Giordani, Mattia Bossi, Abhiram Rajan, Niki Di Giano,<br \/>Riccardo Albiero, Francesco Ceccarelli, Roberto Osellame,<br \/>Ulysse Chabaud, Fabio Sciarrino and Elham Kashefi<\/p><p>Abstract: Linear optical architectures have been extensively<br \/>investigated for quantum computing and quantum machine<br \/>learning applications. Recently, proposals for photonic<br \/>quantum machine learning have combined linear optics with<br \/>resource adaptivity, such as adaptive circuit<br \/>reconfiguration, which promises to enhance expressivity and<br \/>improve algorithm performances and scalability. Moreover,<br \/>linear optical platforms preserve some subspaces due to the<br \/>fixed number of particles during the computation, a<br \/>property recently exploited to design a novel quantum<br \/>convolutional neural networks. This last architecture has<br \/>shown an advantage in terms of running time complexity and<br \/>of the number of parameters needed with respect to other<br \/>quantum neural network proposals. We propose to present the<br \/>results from two papers of our team. First, we propose a<br \/>new scheme that relies on state injection, a<br \/>measurement-based technique that can produce states that<br \/>are more controllable, and solve learning tasks that are<br \/>believed to be intractable classically. Secondly, we design<br \/>and experimentally implement the first photonic quantum<br \/>convolutional neural network (PQCNN) architecture based on<br \/>particle-number preserving circuits equipped with state<br \/>injection. Subsequently, we experimentally validate the<br \/>PQCNN for an image classification on a photonic platform<br \/>utilizing a semiconductor quantum dot-based single-photon<br \/>source and programmable integrated photonic interferometers<br \/>comprising 8 and 12 modes. We highlight the potential<br \/>utility of a simple adaptive technique for a nonlinear<br \/>Boson Sampling task, compatible with near-term quantum<br \/>devices. Such approach open the path to new QML algorithms<br \/>with useful polynomial advantages, as such photonic<br \/>architecture present a very low running time.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1140-span-stylecolor919fd1qnnspandesigning-quantum-machine-learning-models-for-graphs\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"3\" aria-controls=\"elementor-tab-content-2533\"><span class=\"eael-accordion-tab-title\">11:40 <span style=\"color:#919FD1\">(QNN)<\/span>Designing quantum machine learning models for graphs<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-2533\" class=\"eael-accordion-content clearfix\" data-tab=\"3\" aria-labelledby=\"1140-span-stylecolor919fd1qnnspandesigning-quantum-machine-learning-models-for-graphs\"><p>Authors: Frederic Sauvage, Pranav Kalidindi, Frederic Rapp and<br \/>Martin Larocca<\/p><p>Abstract: Geometric Machine Learning (GML) successes have been<br \/>achieved through thorough study and design of new<br \/>equivariant neural networks. In comparison, geometric<br \/>quantum machine learning (GQML) models critically lacks<br \/>such a detailed understanding and a unifying perspective on<br \/>their design remains elusive. Focusing on GQML models for<br \/>graph datasets, we show that a comprehensive<br \/>characterisation of their constituents is possible.<br \/>We further probe benefits of this toolbox including the<br \/>generalization of known models,<br \/>sometimes at virtually no cost, and straightforward<br \/>classical pre-training strategies.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1200-span-stylecolor919fd1qnnspanaccelerating-inference-for-multilayer-convolutional-neural-networks-with-quantum-computers\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"4\" aria-controls=\"elementor-tab-content-2534\"><span class=\"eael-accordion-tab-title\">12:00 <span style=\"color:#919FD1\">(QNN)<\/span>Accelerating Inference for Multilayer Convolutional Neural Networks with Quantum Computers<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-2534\" class=\"eael-accordion-content clearfix\" data-tab=\"4\" aria-labelledby=\"1200-span-stylecolor919fd1qnnspanaccelerating-inference-for-multilayer-convolutional-neural-networks-with-quantum-computers\"><p>Authors: Arthur Rattew, Po-Wei Huang, Naixu Guo, Lirand\u00eb Pira and<br \/>Patrick Rebentrost<\/p><p>Abstract: Fault-tolerant Quantum Processing Units (QPUs) promise to<br \/>deliver exponential speed-ups in select computational<br \/>tasks, but a clear recipe for integrating them into<br \/>existing classical deep-learning pipelines is missing. In<br \/>this work, we focus on the setting of accelerating<br \/>inference, where a pre-trained network outputs a<br \/>probability distribution over possible outputs (e.g.,<br \/>classes or tokens), and ask: for which architectures, and<br \/>under which Quantum Random Access Memory (QRAM)<br \/>assumptions, can a fault-tolerant QPU asymptotically<br \/>outperform classical hardware? All the networks we consider<br \/>are composed of fundamental residual blocks which consist<br \/>of regularized multi-filter 2D convolutions, sigmoid<br \/>activations, skip-connections, and layer normalisations,<br \/>echoing the structure of ResNet.<\/p><p>To give end-to-end complexity bounds taking into account<br \/>both input and memory assumptions, we consider three<br \/>regimes (where the first two assume that QRAM is feasible).<br \/>In regime 1, both the input tensor and network weights are<br \/>given by QRAM. We prove that a network with an<br \/>$N$-dimensional vectorized input, $d$ residual block<br \/>layers, and a final residual-linear-pooling layer can be<br \/>implemented with $O(\\text{polylog}(N)^d)$ inference cost,<br \/>and suggest that known dequantization methods do not apply.<br \/>Examples of tasks covered by this setting include those<br \/>with repeated calls to a slowly-changing input, such as is<br \/>the case with multi-turn LLM chat. Regime 2 keeps only the<br \/>weights in QRAM; the input must be loaded at $O(N)$ cost.<br \/>Even so, shallow multi-layer bilinear-style networks can<br \/>achieve quartic (or better) speedups. Regime 3 has no QRAM,<br \/>and so quantum speedup is unlikely in terms of either the<br \/>dimension of the input, or the number of network<br \/>parameters, and must instead exploit high dimensional<br \/>latent feature transforms.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1230-lunch\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"5\" aria-controls=\"elementor-tab-content-2535\"><span class=\"eael-accordion-tab-title\">12:30 Lunch<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-2535\" class=\"eael-accordion-content clearfix\" data-tab=\"5\" aria-labelledby=\"1230-lunch\"><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1400-span-stylecolor919fd1ltspantolerant-testing-of-stabilizer-states-with-mixed-state-inputs\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"6\" aria-controls=\"elementor-tab-content-2536\"><span class=\"eael-accordion-tab-title\">14:00 <span style=\"color:#919FD1\">(LT)<\/span>Tolerant Testing of Stabilizer States with Mixed State Inputs<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-2536\" class=\"eael-accordion-content clearfix\" data-tab=\"6\" aria-labelledby=\"1400-span-stylecolor919fd1ltspantolerant-testing-of-stabilizer-states-with-mixed-state-inputs\"><p>Authors: Daniel Liang and Vishnu Iyer<\/p><p>Abstract: We study the problem of tolerant testing of stabilizer<br \/>states. In particular, we give the first such algorithm<br \/>that accepts mixed state inputs. Formally, given a mixed<br \/>state \u03c1 that either has fidelity at least \u03b51 with some<br \/>stabilizer pure state or fidelity at most \u03b52 with all such<br \/>states, where \u03b52 \u2264 \u03b51^O(1), our algorithm distinguishes the<br \/>two cases with sample complexity poly(1\/\u03b51) and time<br \/>complexity O(n * poly(1\/\u03b51)).<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1420-span-stylecolor919fd1qnnspanquantum-advantage-in-learning-quantum-dynamics\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"7\" aria-controls=\"elementor-tab-content-2537\"><span class=\"eael-accordion-tab-title\">14:20 <span style=\"color:#919FD1\">(QNN)<\/span>Quantum Advantage in Learning Quantum Dynamics<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-2537\" class=\"eael-accordion-content clearfix\" data-tab=\"7\" aria-labelledby=\"1420-span-stylecolor919fd1qnnspanquantum-advantage-in-learning-quantum-dynamics\"><p>Authors: Alice Barthe, Mahtab Yaghubi, Michele Grossi and Vedran Dunjko<\/p><p>Abstract: One of the key challenges in quantum machine learning is<br \/>finding relevant machine learning tasks with a provable<br \/>quantum advantage.<br \/>A natural candidate for this is learning unknown<br \/>Hamiltonian dynamics.<br \/>Here, we tackle the supervised learning version of this<br \/>problem, where we are given random examples of the inputs<br \/>to the dynamics as classical data, paired with the<br \/>expectation values of some observable after the time<br \/>evolution, as corresponding labels (outputs). The task is<br \/>to replicate the corresponding input-output function.<br \/>We prove that this task can yield provable exponential<br \/>classical-quantum learning advantages under common<br \/>complexity assumptions in natural settings.<br \/>To design our quantum learning algorithms, we introduce a<br \/>new method, which we call Fourier coefficient sampling for<br \/>parametrized circuit functions, and which may be of<br \/>independent interest.<br \/>Furthermore, we discuss the limitations of generalizing our<br \/>method to arbitrary quantum dynamics while maintaining<br \/>provable guarantees.<br \/>We explain that significant generalizations are impossible<br \/>under certain complexity-theoretic assumptions, but<br \/>nonetheless, we provide a heuristic kernel method, where we<br \/>trade-off provable correctness for broader applicability.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1440-span-stylecolor919fd1qnnspantesting-classical-properties-from-quantum-data\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"8\" aria-controls=\"elementor-tab-content-2538\"><span class=\"eael-accordion-tab-title\">14:40 <span style=\"color:#919FD1\">(QNN)<\/span>Testing classical properties from quantum data<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-2538\" class=\"eael-accordion-content clearfix\" data-tab=\"8\" aria-labelledby=\"1440-span-stylecolor919fd1qnnspantesting-classical-properties-from-quantum-data\"><p>Authors: Matthias C. Caro, Preksha Naik and Joseph Slote<\/p><p>Abstract: Testing properties of Boolean functions is often<br \/>dramatically faster than learning. However, this advantage<br \/>usually disappears when testers are limited to random<br \/>samples of the function&#8212;a natural setting for data<br \/>science&#8212;rather than adaptive queries. In this work we<br \/>investigate the \\emph{quantum} version of this &#8220;&#8221;data<br \/>science scenario&#8221;&#8221;: quantum algorithms that test properties<br \/>of a function $f$ solely from quantum data in the form of<br \/>copies of the function state $\\ket{f}\\propto<br \/>\\sum_x\\ket{x,f(x)}$.<\/p><p>\\textbf{New tests.} For three well-established<br \/>properties&#8212;monotonicity, symmetry, and<br \/>triangle-freeness&#8212;we show that the speedup lost when<br \/>restricting classical testers to sampled data can be<br \/>recovered by considering quantum data.<\/p><p>\\textbf{Inadequacy of Fourier sampling.} Our new testers<br \/>use techniques beyond quantum Fourier sampling, and we show<br \/>that this necessary. In particular, there is no<br \/>constant-complexity tester for symmetry relying solely on<br \/>Fourier sampling and random classical samples.<\/p><p>\\textbf{Classical queries vs. quantum data.} We exhibit a<br \/>testing problem that can be solved from $\\mathcal{O}(1)$<br \/>classical queries but that requires $\\Omega(2^{n\/2})$<br \/>function state copies. The \\textsc{Forrelation} problem<br \/>provides a separation of the same magnitude in the opposite<br \/>direction, so we conclude that quantum data and classical<br \/>queries are &#8220;&#8221;maximally incomparable&#8221;&#8221; resources for testing.<\/p><p>\\textbf{Towards lower bounds.} We also begin the study of<br \/>\\emph{lower bounds} for testing from quantum data. For<br \/>quantum monotonicity testing, we prove that the ensembles<br \/>used to prove exponential lower bounds for classical<br \/>sample-based testing, do not yield any nontrivial lower<br \/>bounds for testing from quantum data. New insights specific<br \/>to quantum data will be required for proving copy<br \/>complexity lower bounds for testing in this model.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1500-coffee\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"9\" aria-controls=\"elementor-tab-content-2539\"><span class=\"eael-accordion-tab-title\">15:00 Coffee<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-2539\" class=\"eael-accordion-content clearfix\" data-tab=\"9\" aria-labelledby=\"1500-coffee\"><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1530-span-stylecolor919fd1qnnspanthe-abelian-state-hidden-subgroup-problem-learning-stabilizer-groups-and-beyond\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"10\" aria-controls=\"elementor-tab-content-25310\"><span class=\"eael-accordion-tab-title\">15:30 <span style=\"color:#919FD1\">(QNN)<\/span>The abelian state hidden subgroup problem: Learning stabilizer groups and beyond<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-25310\" class=\"eael-accordion-content clearfix\" data-tab=\"10\" aria-labelledby=\"1530-span-stylecolor919fd1qnnspanthe-abelian-state-hidden-subgroup-problem-learning-stabilizer-groups-and-beyond\"><p>Authors: Marcel Hinsche, Jens Eisert and Jose Carrasco<\/p><p>Abstract: Identifying the symmetry properties of quantum states is a<br \/>central theme in quantum information theory and quantum<br \/>many-body physics. In this work, we investigate quantum<br \/>learning problems in which the goal is to identify a hidden<br \/>symmetry of an unknown quantum state. Building on the<br \/>recent formulation of the state hidden subgroup problem<br \/>(StateHSP), we focus on abelian groups and develop a<br \/>quantum algorithmic approach to efficiently learn any<br \/>hidden symmetry subgroup in this case. We showcase the<br \/>versatility of the approach in three concrete applications:<br \/>These are learning (i) qubit and qudit stabilizer groups,<br \/>(ii) cuts along which a state is unentangled, and (iii)<br \/>hidden translation symmetries. Taken together, our results<br \/>underscore the potential of the StateHSP framework as a<br \/>foundation for broader symmetry-based quantum learning<br \/>tasks.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1550-span-stylecolor919fd1qnnspanquantum-recurrent-embedding-neural-network-\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"11\" aria-controls=\"elementor-tab-content-25311\"><span class=\"eael-accordion-tab-title\">15:50 <span style=\"color:#919FD1\">(QNN)<\/span>Quantum Recurrent Embedding Neural Network <\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-25311\" class=\"eael-accordion-content clearfix\" data-tab=\"11\" aria-labelledby=\"1550-span-stylecolor919fd1qnnspanquantum-recurrent-embedding-neural-network-\"><p>Authors: Mingrui Jing, Erdong Huang, Xiao Shi, Shengyu Zhang and Xin Wang<\/p><p>Abstract: Quantum neural networks (QNNs) hold promise for leveraging quantum systems to tackle complex learning tasks, but their scalability has been hindered by severe trainability issues such as barren plateaus. In this work, we introduce the Quantum Recurrent Embedding Neural Network (QRENN), a novel quantum architecture inspired by fast-track information<br \/>pathways in ResNets and grounded in general quantum circuit design principles. By employing tools from dynamical Lie algebra theory, we rigorously prove that QRENN circuits are trainable and can avoid barren plateaus. We validate the practical power of QRENN by applying it to two challenging quantum supervised learning tasks: classifying quantum Hamiltonians and detecting symmetry-protected topological (SPT) phases. Our model demonstrates high accuracy and robustness in both settings, showcasing its ability to<br \/>learn nontrivial quantum features from data, suggesting a<br \/>promising path toward scalable and reliable quantum machine<br \/>learning models.<\/p><\/div>\n\t\t\t\t\t<\/div><div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1610-span-stylecolor919fd1qnnspanan-efficient-approach-to-realize-quantum-random-features-\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"12\" aria-controls=\"elementor-tab-content-25312\"><span class=\"eael-accordion-tab-title\">16:10 <span style=\"color:#919FD1\">(QNN)<\/span>An efficient approach to realize Quantum Random Features <\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-25312\" class=\"eael-accordion-content clearfix\" data-tab=\"12\" aria-labelledby=\"1610-span-stylecolor919fd1qnnspanan-efficient-approach-to-realize-quantum-random-features-\"><p>Authors: Akitada Sakurai, Aoi Hayashi, William Munro and Kae Nemoto<\/p><p>Abstract: It has recently been shown that several quantum machine<br \/>learning models, including Quantum Circuit Learning (QCL),<br \/>Quantum Reservoir Computing (QRC), and Quantum Extreme<br \/>Learning Machines (QELM), can be analyzed via the Fourier<br \/>series. In such works, the data-encoding circuit determines<br \/>the accessible frequency components, and increasing circuit<br \/>depth expands the representational capacity. However, these<br \/>models face challenges in practical applications due to the<br \/>lack of control over which frequency components are needed<br \/>for a given task. In our talk, we propose a practical<br \/>QRC\/QELM architecture inspired by Random Fourier Features<br \/>(RFF). Our model utilizes layered quantum circuits with<br \/>Z-rotation encoders and fixed permutation unitaries to<br \/>efficiently generate RFF-like frequency structures. We<br \/>demonstrate that our method requires only $\\mathcal{O}(log<br \/>N_f\\cdot L)$ computational cost to generate $N_f$ features<br \/>in preprocessing, compared to $\\mathcal{O}(N_f)$ in<br \/>classical RFF. Furthermore, the model remains robust when<br \/>the permutation circuit is replaced with more general<br \/>quantum dynamics. These results provide practical design<br \/>principles for constructing expressive and scalable QML<br \/>models, applicable to tasks such as image classification.<\/p><\/div>\n\t\t\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-28f4432 e-flex e-con-boxed e-con e-parent\" data-id=\"28f4432\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3aecfec elementor-widget elementor-widget-eael-adv-accordion\" data-id=\"3aecfec\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"eael-adv-accordion.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t            <div class=\"eael-adv-accordion\" id=\"eael-adv-accordion-3aecfec\" data-scroll-on-click=\"no\" data-scroll-speed=\"300\" data-accordion-id=\"3aecfec\" data-accordion-type=\"toggle\" data-toogle-speed=\"300\">\n            <div class=\"eael-accordion-list\">\n\t\t\t\t\t<div id=\"1630-conference-ends\" class=\"elementor-tab-title eael-accordion-header\" tabindex=\"0\" data-tab=\"1\" aria-controls=\"elementor-tab-content-6171\"><span class=\"eael-accordion-tab-title\">16:30 Conference Ends<\/span><svg aria-hidden=\"true\" class=\"fa-toggle e-font-icon-svg e-fas-angle-right\" viewBox=\"0 0 256 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z\"><\/path><\/svg><\/div><div id=\"elementor-tab-content-6171\" class=\"eael-accordion-content clearfix\" data-tab=\"1\" aria-labelledby=\"1630-conference-ends\"><p>QTML 2025 is finished. See you for the next edition in 2026!<\/p><\/div>\n\t\t\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t                    <\/div>\n\t\t                    <\/div>\n        <\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-1cffdf8 e-flex e-con-boxed e-con e-parent\" data-id=\"1cffdf8\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8898308 elementor-widget elementor-widget-text-editor\" data-id=\"8898308\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"color: #f6851f;\">LT <\/span> &#8211; Learning Theory<br \/><span style=\"color: #f6851f;\">QNN<\/span> &#8211; Variational QML<br \/><span style=\"color: #f6851f;\">QA<\/span> &#8211; Fault tolerant\/quantum algos<br \/><span style=\"color: #f6851f;\">PHY<\/span> &#8211; ML for quantum, physics<br \/><span style=\"color: #f6851f;\">TOM<\/span> &#8211; shadows\/tomography<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-39aaae2 e-flex e-con-boxed e-con e-parent\" data-id=\"39aaae2\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8646a3e elementor-widget elementor-widget-spacer\" data-id=\"8646a3e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div 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https:\/\/qtml2025.cqt.sg\/wp-content\/uploads\/2025\/09\/QAI_Logo_with_Symbol_Black_Horizontal-1024x253.jpg 1024w, https:\/\/qtml2025.cqt.sg\/wp-content\/uploads\/2025\/09\/QAI_Logo_with_Symbol_Black_Horizontal-768x189.jpg 768w, https:\/\/qtml2025.cqt.sg\/wp-content\/uploads\/2025\/09\/QAI_Logo_with_Symbol_Black_Horizontal.jpg 1403w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3f3d92c e-grid e-con-boxed e-con e-child\" data-id=\"3f3d92c\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5ec4ed6 elementor-widget elementor-widget-image\" data-id=\"5ec4ed6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/www.quantinuum.com\/\">\n\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"56\" src=\"https:\/\/qtml2025.cqt.sg\/wp-content\/uploads\/2025\/10\/Quantinuum-Logo__horizontal-black-300x56.png\" class=\"attachment-medium size-medium wp-image-1511\" alt=\"\" srcset=\"https:\/\/qtml2025.cqt.sg\/wp-content\/uploads\/2025\/10\/Quantinuum-Logo__horizontal-black-300x56.png 300w, https:\/\/qtml2025.cqt.sg\/wp-content\/uploads\/2025\/10\/Quantinuum-Logo__horizontal-black-768x143.png 768w, https:\/\/qtml2025.cqt.sg\/wp-content\/uploads\/2025\/10\/Quantinuum-Logo__horizontal-black.png 851w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e78732d elementor-widget elementor-widget-image\" data-id=\"e78732d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/www.xanadu.ai\/\">\n\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"77\" src=\"https:\/\/qtml2025.cqt.sg\/wp-content\/uploads\/2025\/09\/Xanadu-Logo-Landscape-3-300x77.png\" class=\"attachment-medium size-medium wp-image-1186\" alt=\"\" srcset=\"https:\/\/qtml2025.cqt.sg\/wp-content\/uploads\/2025\/09\/Xanadu-Logo-Landscape-3-300x77.png 300w, https:\/\/qtml2025.cqt.sg\/wp-content\/uploads\/2025\/09\/Xanadu-Logo-Landscape-3-1024x262.png 1024w, https:\/\/qtml2025.cqt.sg\/wp-content\/uploads\/2025\/09\/Xanadu-Logo-Landscape-3-768x196.png 768w, https:\/\/qtml2025.cqt.sg\/wp-content\/uploads\/2025\/09\/Xanadu-Logo-Landscape-3-1536x393.png 1536w, https:\/\/qtml2025.cqt.sg\/wp-content\/uploads\/2025\/09\/Xanadu-Logo-Landscape-3-2048x523.png 2048w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-bbf1386 e-flex e-con-boxed e-con e-parent\" data-id=\"bbf1386\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Schedule Watch recordings of keynote, invited and contributed talks here Sunday Monday Tuesday Wednesday Thursday Friday Location: National University of [&hellip;]<\/p>\n","protected":false},"author":5,"featured_media":506,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_eb_attr":"","site-sidebar-layout":"no-sidebar","site-content-layout":"","ast-site-content-layout":"full-width-container","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"disabled","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center 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