[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123461-en":3,"doc-seo-123461-105":30,"detail-sidebar-cat-0-en-105":91},{"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},123461,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Circumventing Traps in Analog Quantum Machine Learning Algorithms Through Co-Design","Quantum machine learning (QML) aims to enable near-term advantages on noisy intermediate-scale devices. While many QML methods rely on quantum circuit abstractions, analog quantum machine learning (AQML) uses natural system dynamics for computation and is motivated by potential noise resilience and application-specific performance. The study examines AQML loss landscapes for two model classes, finds trap-free behavior only in one, and notes both violate key quantum optimal control assumptions. A co-design method based on the ansatz Magnus expansion improves convergence for unitary evolution simulation with applications in metrology and quantum chemistry.","arXiv :2408 . 14697v1 [ quant-ph] 26 Aug 2024  \nCircumventing Traps in Analog Quantum Machine Learning Algorithms Through Co-Design  \nRodrigo Araiza Bravo‡, 1 Jorge Garcia Ponce‡, 1 Hong-Ye Hu,1 and Susanne F. Yelin1  \nDepartment of Physics, Harvard University, Cambridge, MA 02138 USA  \n(*Electronic mail: [jorgegarciaponce@college.harvard.edu](jorgegarciaponce@college.harvard.edu))  \n(*Electronic mail: [oaraizabravo@g.harvard.edu](oaraizabravo@g.harvard.edu))  \n(Dated: 28 August 2024)  \nQuantum machine learning QML algorithms promise to deliver near-term, applicable quantum computation on noisy, intermediate-scale systems. While most of these algorithms leverage quantum circuits for generic applications, a recent set of proposals, called analog quantum machine learning (AQML) algorithms, breaks away from circuit-based abstractions and favors leveraging the natural dynamics of quantum systems for computation, promising to be noise-resilient and suited for specific applications such as quantum simulation. Recent AQML studies have called for determining best ansatz selection practices and whether AQML algorithms have trap-free landscapes based on theory from quantum optimal control (QOC) . We address this call by systematically studying AQML landscapes on two models: those admitting black-boxed expressivity and those tailored to simulating a specific unitary evolution. Numerically, the first kind exhibits local traps in their landscapes, while the second kind is trap-free. However, both kinds violate QOC theory’s key assumptions for guaranteeing trap-free landscapes. We propose a methodology to co-design AQML algorithms for unitary evolution simulation using the ansatz’s Magnus expansion. We show favorable convergence in simulating dynamics with applications to metrology and quantum chemistry. We conclude that such co-design is necessary to ensure the applicability of AQML algorithms.  \nI. INTRODUCTION  \nQuantum machine learning (QML) promises to deliver advantageous applications in noisy, intermediate-scale quantum computers. QML algorithms promise to deliver either by using quantum systems to speed up machine learning subroutines 1,2 or by designing novel techniques to optimize and control noisy quantum systems for machine learning and quantum applications3. This later approach, called variational quantum algorithms, uses variational optimization for application within near-term devices. These algorithms often comprise an underlying hardware architecture with tunable parameters, a loss function measuring the error relative to a desired computation, and a classical optimizer routine tuning the parameters to minimize the loss. The hardware architecture is often abstracted away and modeled as a digital quantum circuit, and such abstracted algorithms are called variational quantum circuits (VQCs) .  \nDespite their promise, VQCs exhibit issues with accuracy, efficiency, and training, which precludes an advantage over classical algorithms. VQCs often experience flat landscapes upon random initialization4 ,– a phenomenon known as barren plateaus – and an exponential number of local minima5. To circumvent these challenges, extensive work has been done on proposing circuit architectures (ansätze)6–10 , loss functions 11–14 , regulation techniques 15–17 , and optimization techniques 18–21.  \nA recent set of proposals, called analog quantum machine learning (AQML) ansätze22–24 , breaks away from circuitbased abstractions. Instead, AQML favors directly using  \n‡ These authors contributed equally to this work.  \nthe system’s dynamics for computation. At a high level, an AQML ansatz comprises a native interaction Hamiltonian anda set of time-dependent controls. However, AQML studies suffer from various practical drawbacks, such as the fact that simulating time evolution is computationally expensive and, therefore, limits theoretical studies to small system sizes.  \nAQML appeals to developers for various reasons related to classical analog co","cbCaiuMbnOCNZ672","https://ap.wps.com/l/cbCaiuMbnOCNZ672","pdf",4603874,1,23,"English","en",105,"# Introduction\n## Quantum machine learning and variational quantum algorithms\n## Variational quantum circuits: barren plateaus and local minima\n## Analog quantum machine learning: ansatz structure and motivations\n## Study objective and landscape motivation","[{\"question\":\"What makes analog quantum machine learning different from circuit-based QML?\",\"answer\":\"AQML departs from abstract quantum-circuit implementations by using the system’s natural dynamics. Its ansatz is built from a native interaction Hamiltonian plus time-dependent control terms, rather than a purely gate-based circuit model.\"},{\"question\":\"How do the paper’s two AQML model classes behave regarding traps in the loss landscape?\",\"answer\":\"Numerical results show that the class with black-boxed expressivity exhibits local traps, while the class tailored to simulating a specific unitary evolution is trap-free.\"},{\"question\":\"Why does the proposed co-design approach matter for ensuring trap-free applicability?\",\"answer\":\"Both AQML classes violate key assumptions from quantum optimal control theory that normally guarantee trap-free landscapes. The paper introduces a co-design methodology using the ansatz’s Magnus expansion, yielding favorable convergence for unitary evolution simulation and enabling practical applications.\"}]","Circumventing Traps in Analog Quantum Machine Learning Algorithms Through Co-Design | PDF",1785816647,58,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"circumventing-traps-in-analog-quantum-machine-learning-algorithms-through-co-design","",{"@graph":36,"@context":85},[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/circumventing-traps-in-analog-quantum-machine-learning-algorithms-through-co-design/123461/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What makes analog quantum machine learning different from circuit-based QML?","Question",{"text":75,"@type":76},"AQML departs from abstract quantum-circuit implementations by using the system’s natural dynamics. Its ansatz is built from a native interaction Hamiltonian plus time-dependent control terms, rather than a purely gate-based circuit model.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do the paper’s two AQML model classes behave regarding traps in the loss landscape?",{"text":80,"@type":76},"Numerical results show that the class with black-boxed expressivity exhibits local traps, while the class tailored to simulating a specific unitary evolution is trap-free.",{"name":82,"@type":73,"acceptedAnswer":83},"Why does the proposed co-design approach matter for ensuring trap-free applicability?",{"text":84,"@type":76},"Both AQML classes violate key assumptions from quantum optimal control theory that normally guarantee trap-free landscapes. The paper introduces a co-design methodology using the ansatz’s Magnus expansion, yielding favorable convergence for unitary evolution simulation and enabling practical applications.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]