[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118103-en":3,"doc-seo-118103-105":30,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118103,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Dynamical simulation via quantum machine learning with provable generalization","Framework for enhancing dynamical simulation on near-term quantum hardware by leveraging quantum machine learning. The work employs generalization bounds to rigorously relate training-data requirements to unseen-data prediction error, enabling resource-efficient circuit training in qubit and data usage. A proposed “resource-efficient fast-forwarding” (REFF) approach learns a fixed-depth circuit for fast-forwarding long-time quantum dynamics. Preliminary numerics on the XY model show efficient scaling with problem size and simulations reaching 20× longer than Trotterization on IBMQ-Bogota.","Dynamical simulation via quantum machine learning with provable generalization  \nJoe Gibbs  , 1, 2 , * Zoë Holmes,3, 4 , * Matthias C. Caro  ,5, 6, 7, 8 Nicholas Ezzell,3, 9 Hsin-Yuan Huang  ,8, 10 Lukasz Cincio, 11  \nAndrew T. Sornborger  ,3 and Patrick J. Coles 11  \n1 Department of Physics, University of Surrey, Guildford GU2 7XH, United Kingdom  \n2 AWE, Aldermaston, Reading RG7 4PR, United Kingdom  \n3 Information Sciences, Los Alamos National Laboratory, Los Alamos, New Mexico, USA  \n4 Institute of Physics, Ecole Polytechnique Fédéderale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland  \n5 Department of Mathematics, Technical University of Munich, Garching, Germany  \n6 Munich Center for Quantum Science and Technology (MCQST), Munich, Germany  \n7 Dahlem Center for Complex Quantum Systems, Freie Universität Berlin, Berlin, Germany  \n8 Institute for Quantum Information and Matter, Caltech, Pasadena, California, USA  \n9 Department of Physics & Astronomy, University of Southern California, Los Angeles, California, USA  \n10 Department of Computing and Mathematical Sciences, Caltech, Pasadena, California, USA  \n11 Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, USA  \n (Received 4 October 2022; revised 8 September 2023; accepted 11 January 2024; published 5 March 2024)  \nMuch attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to enhance dynamical simulations has not been thoroughly investigated. Here we develop a framework for using QML methods to simulate quantum dynamics on near-term quantum hardware. We use generalization bounds, which bound the error a machine learning model makes on unseen data, to rigorously analyze the training data requirements of an algorithm within this framework. Our algorithm is thus resource efficient in terms of qubit and data requirements. Furthermore, our preliminary numerics for the XY model exhibit efficient scaling with problem size, and we simulate 20 times longer than Trotterization on IBMQ-Bogota.  \nDOI: 10.1103/PhysRevResearch.6.013241  \nI. INTRODUCTION  \nThe exponential speedup of dynamical quantum simulation provided the original motivation for quantum computers [1,2] . In the long term, large-scale quantum simulations are expected to transform fields such as materials science, chemistry, and high-energy physics. Nearer term, since efficient classical dynamical simulation methods are lacking (in contrast to those for computing static quantum properties like electronic structure), dynamical simulation may plausibly be one of the first applications to see quantum advantage.  \nAchieving near-term quantum advantage for dynamics will require long-time simulations on noisy intermediatescale quantum (NISQ) hardware [3] . Standard methods like Trotterization grow the circuit depth in proportion to the simulation time, ultimately running into the decoherence time of the NISQ device [4,5] . Fast-forwarding methods for long-time simulations on NISQ devices have recently been introduced [6–9], but are limited by various inefficiencies (e.g., qubit and data requirements) . Here, we address these  \n*These authors contributed equally to this work.  \nPublished by the American Physical Society under the terms of the Creative Commons Attribution 4 .0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.  \ninefficiencies, potentially opening the door for near-term quantum advantage.  \nIn parallel to these developments, quantum machine learning (QML) [10, 11] has emerged as another potential application for quantum advantage [12] . At its core, QML involves using classical or quantum data to train a parameterized quantum circuit. A number of promising paradigms for training are being pursued, including variational quantum algorithms using training data [13], quan","cbCairmIzG5dmNg1","https://ap.wps.com/l/cbCairmIzG5dmNg1","pdf",1303183,1,17,"English","en",105,"# Introduction\n## Quantum advantage for dynamical simulation\n## Combining QML with dynamical simulation\n## Generalization bounds and training-data requirements\n## Resource-efficient fast-forwarding (REFF)\n# General Framework","[{\"question\":\"What numerical demonstration is included?\",\"answer\":\"Preliminary numerics for the XY model show efficient scaling with problem size, and the algorithm simulates 20 times longer than Trotterization on IBMQ-Bogota.\"}]","Dynamical simulation via quantum machine learning with provable generalization | PDF",1785681637,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":28},"dynamical-simulation-via-quantum-machine-learning-with-provable-generalization","",{"@graph":36,"@context":77},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/dynamical-simulation-via-quantum-machine-learning-with-provable-generalization/118103/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"What numerical demonstration is included?","Question",{"text":75,"@type":76},"Preliminary numerics for the XY model show efficient scaling with problem size, and the algorithm simulates 20 times longer than Trotterization on IBMQ-Bogota.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,127],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":98,"slug":130},19,"General","general"]