[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116850-en":3,"doc-seo-116850-105":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},116850,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Quantum Machine Learning Implementations: Proposals and Experiments","Overview and forward-looking discussion of recent theoretical proposals and experimental implementations in quantum machine learning. The focus covers high-impact topics including quantum reinforcement learning, quantum autoencoders, and quantum memristors, demonstrating how these ideas are realized on quantum photonics and superconducting-circuit platforms. The work argues for advancing early NISQ implementations to enable near-term, industry- and society-relevant machine-learning calculations, even before scalable quantum computers mature.","11 Mar 2023  \nQuantum Machine Learning Implementations: Proposals and Experiments  \nLucas Lamata* Prof. L. Lamata  \nDepartamento de F􀀓􀀐sica At􀀓omica, Molecular, y Nuclear, Facultad de F􀀓􀀐sica, Universidad de Sevilla, Apartado 1065, 41080 Sevilla, Spain, and Instituto Carlos I de F􀀓􀀐sica Te􀀓orica y Computacional, 18071 Granada, Spain  \nEmail Address: llamata@us.es  \nKeywords: Quantum Arti􀀌cial Intelligence, Quantum Machine Learning, Implementations of Quantum Information, Quantum Technologies, Quantum Photonics, Superconducting Circuits  \nThis article gives an overview and a perspective of recent theoretical proposals and their experimental implementations in the 􀀌eld of quantum machine learning. Without an aim to being exhaustive, the article reviews speci􀀌c high-impact topics such as quantum reinforcement learning, quantum autoencoders, and quantum memristors, and their experimental realizations in the platforms of quantum photonics and superconducting circuits. The 􀀌eld of quantum machine learning could be among the 􀀌rst quantum technologies producing results that are bene􀀌cial for industry and, in turn, to society. Therefore, it is necessary to push forward initial quantum implementations of this technology, in Noisy Intermediate-Scale Quantum Computers, aiming for achieving fruitful calculations in machine learning that are better than with any other current or future computing paradigm.  \narXiv :2303 .06263v1  \n[1-8] . Some textbooks in the 􀀌eld for more introductory topics are Refs. [9,10] .  \nEven though theoretical results more related to computer science are important in the 􀀌eld, as they can show more easily speedup evidence via complexity-class arguments, in our view it is always important to carry out proposals for implementations, for nearer-term devices instead of full-􀀍edged scalable quantum computers, because i) this can motivate experimental groups to push a bit further their technologies to be able to implement these proposals in the short term, and ii) there is some hope inside the quantum machine learning community that with Noisy Intermediate-Scale Quantum computers (NISQ) [11] one may be able to already achieve some kind of quantum speedup, and produce results which are useful for industry and society. In this sense, in this article we will review some theory proposals in the 􀀌eld of quantum machine learning, together with their experimental implementations in the quantum platforms of quantum photonics and superconducting circuits, which are two platforms that seem particularly well suited for quantum machine learning applications.  \nWith this article we do not intend to give a thorough account of the existing literature, or even cover most of it, which is already very extensive, but to select a few theory results which have been carried out in the lab and describe them in some detail. We will focus on three proposals and their respective quantum experiments: quantum reinforcement learning, quantum autoencoders, and quantum memristors. These topics were reviewed in Ref. [6] in a more general way and with no focus on implementations. Here, we will emphasize more the experimental realizations.  \nIn Section 2 we will describe proposals for quantum reinforcement learning and their experimental implementations with quantum photonics and superconducting circuits. In Section 3, we will review the topic of quantum autoencoders via quantum adders, and an experiment with superconducting circuits, and in Section 4 we will revisit the concept of photonic quantum memristor, as well as its implementation with a quantum photonics system. Finally, in Section 5 we will give the Conclusions.  \nFigure 1: Scheme of Quantum Reinforcement Learning. Reproduced under terms of the CC-BY license. [Ref. 15] . Copyright the Author, 2017 . Springer Nature.  \n2 Quantum Reinforcement Learning  \nThe 􀀌eld of Quantum Reinforcement Learning deals with analyzing quantum agents that interact with their outer world, so named \\environment\" (no","cbCaili3G4OwkgL4","https://ap.wps.com/l/cbCaili3G4OwkgL4","pdf",1117182,1,11,"English","en",105,"# Quantum Reinforcement Learning\n## Theory Proposals\n# Quantum Autoencoders\n# Quantum Memristors\n# Conclusions","[{\"question\":\"What topics does the document cover in quantum machine learning implementations?\",\"answer\":\"It reviews quantum reinforcement learning, quantum autoencoders, and quantum memristors, emphasizing experimental realizations rather than only theory.\"},{\"question\":\"Which experimental platforms are highlighted for these implementations?\",\"answer\":\"The document highlights quantum photonics and superconducting circuits as platforms well suited for quantum machine learning applications.\"},{\"question\":\"Why does the document focus on NISQ-era implementations instead of scalable quantum computers?\",\"answer\":\"It argues that nearer-term implementations can motivate experimental advances and may already enable some quantum speedup or useful results under NISQ constraints.\"}]","Quantum Machine Learning Implementations: Proposals and Experiments | 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topics does the document cover in quantum machine learning implementations?","Question",{"text":75,"@type":76},"It reviews quantum reinforcement learning, quantum autoencoders, and quantum memristors, emphasizing experimental realizations rather than only theory.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which experimental platforms are highlighted for these implementations?",{"text":80,"@type":76},"The document highlights quantum photonics and superconducting circuits as platforms well suited for quantum machine learning applications.",{"name":82,"@type":73,"acceptedAnswer":83},"Why does the document focus on NISQ-era implementations instead of scalable quantum computers?",{"text":84,"@type":76},"It argues that nearer-term implementations can motivate experimental advances and may already enable some quantum speedup or useful results under NISQ 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