[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117613-en":3,"doc-seo-117613-105":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},117613,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",6,"Technology","First Photon Machine Learning","First Photon Machine Learning提出一种面向人工智能的全新神经网络范式：将量子粒子的双缝实验物理机制扩展到多缝版本，并通过实验建立量子效应相对经典方法的明确优势。实验表明，单光子可在约30%保真度下完成图像识别，显著超过相同规模经典系统理论上限约24%。同时，整个神经网络在亚飞焦量级光学平台实现，等效单次计算能耗低于10−24焦耳，体现量子光学机器学习在速度、容量与能量效率上的潜力。","arXiv :2410 . 17471v1 [ quant-ph] 22 Oct 2024  \nFirst Photon Machine Learning  \nLili Li 1,2 , Santosh Kumar 1,2 , Malvika Garikapati 1,2,3 Yu-Ping Huang 1,2,3∗  \n1Department of Physics, Stevens Institute of Technology, Hoboken, New Jersey 07030, USA.  \n2 Center for Quantum Science and Engineering, Stevens Institute of Technology, Hoboken, NJ, 07030, USA.  \n3 Quantum Computing Inc., Hoboken, New Jersey, 07030, USA.  \n∗ Corresponding author. Email: [yhuang5@stevens.edu](yhuang5@stevens.edu)  \nQuantum techniques are expected to revolutionize how information is acquired, exchanged, and processed. Yet it has been a challenge to realize and measure their values in practical settings. We present first photon machine learning as a new paradigm of neural networks and establish the first unambiguous advantage of quantum effects for artificial intelligence. By extending the physics behind the double-slit experiment for quantum particles to a many-slit version, our experiment finds that a single photon can perform image recognition at around 30% fidelity, which beats by a large margin the theoretical limit of what a similar classical system can possibly achieve (about 24%). In this experiment, the entire neural network is implemented in sub-attojoule optics and the equivalent per-calculation energy cost is below 10−24 joule, highlighting the prospects of quantum optical machine learning for unparalleled advantages in speed, capacity, and energy efficiency.  \n1 INTRODUCTION  \nArtificial intelligence (AI) (1–7) and quantum information science (QIS) (8–13) are at the very front of information technology. Both are pursued intensively by academia and industry, fueled by  \nthe unprecedented information capabilities each promises. Yet their development trajectories have been on quite different paths. AI is crafted on highly parallel and massive data processing using integrated digital circuits, and has quickly flourished in many application areas thanks to the rapid progress in semiconductor manufacturing and the availability of big data (14) . However, its future is shadowed by the exceeding energy consumption and data sizes required for training (15, 16) . Almost on the opposite, QIS can be extremely efficient in data processing and consume orders of magnitude less energy (17–21) . However, QIS devices and systems are hard to scale up, as the manufacturing complexity and operating overhead of quantum devices are much higher than digital circuits (22) .  \nHence, it is natural to ask this question: can QIS and AI complement each other and work together to lay new grounds for information processing (4, 9, 23)? On the one hand, QIS has the potential to significantly increase the energy and data efficiency for AI (24,25). On the other hand, AI can assist QIS in making scalable quantum devices and systems for practical applications (24) . This prospective has spurred lots of studies in this junction, with proposals on quantum convolutional neural networks (26), quantum associative memories (27, 28), quantum-enhanced reinforcement learning (29, 30), quantum variational algorithms (31, 32), and so on. Yet, despite some credible arguments, hitherto there has not been a convincing experiment proof that QIS does give an edge to AI (8, 11) .  \nIn this paper, we present, for the first time, an unambiguous experimental evidence that quantum effects can indeed elevate machine learning above the performance ceiling allowed by any classical means. This is in contrast to previous demonstrations where the insertion of quantum elements in a neural network seems to somewhat improve its performance, but it is unclear if such improvement is material or can be achieved alternatively by better training (6) . Rather, here we show that, for the same problem and using the same resources, even imperfect experimental results from an underoptimized quantum setup can already beat the theoretical performance of an optimal classical counterpart by a significant margin.  \nWe wo","cbCaig05wa9OS31S","https://ap.wps.com/l/cbCaig05wa9OS31S","pdf",1294335,1,19,"English","en",105,"# Introduction\n## AI and quantum information science background\n## Motivation for quantum advantages in machine learning\n## Quantum superposition and many-slit photon experiment","[{\"question\":\"First Photon Machine Learning要解决的核心挑战是什么？\",\"answer\":\"如何在实践场景中实现并度量量子技术对信息获取、交换与处理的真实价值，从而给出可证明确切的量子优势证据。\"},{\"question\":\"实验如何证明量子效应能提升机器学习性能？\",\"answer\":\"通过把双缝实验的量子物理机制扩展为多缝版本，使单光子同时与图像的多个像素发生相互作用，并在相同问题与资源条件下超过经典理论性能上限。\"},{\"question\":\"该方法在能量效率方面的关键结果是什么？\",\"answer\":\"神经网络的实现完全落在量子光学域，单次计算的等效能耗低于10−24焦耳，且决策基于探测单个光子的能量约10−19焦耳。\"}]","First Photon Machine Learning | PDF",1785677282,48,{"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},"first-photon-machine-learning","",{"@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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/first-photon-machine-learning/117613/",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",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"First Photon Machine Learning要解决的核心挑战是什么？","Question",{"text":75,"@type":76},"如何在实践场景中实现并度量量子技术对信息获取、交换与处理的真实价值，从而给出可证明确切的量子优势证据。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"实验如何证明量子效应能提升机器学习性能？",{"text":80,"@type":76},"通过把双缝实验的量子物理机制扩展为多缝版本，使单光子同时与图像的多个像素发生相互作用，并在相同问题与资源条件下超过经典理论性能上限。",{"name":82,"@type":73,"acceptedAnswer":83},"该方法在能量效率方面的关键结果是什么？",{"text":84,"@type":76},"神经网络的实现完全落在量子光学域，单次计算的等效能耗低于10−24焦耳，且决策基于探测单个光子的能量约10−19焦耳。","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},[93,97,101,105,110,113,118,123,128,131,135],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]