[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125837-en":3,"doc-seo-125837-105":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},125837,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Benefits of Open Quantum Systems for Quantum Machine Learning","Open quantum systems can turn environmental dissipation and noise—often treated as obstacles—into operational advantages for quantum machine learning. The work analyzes recent studies on how adapting QML algorithms to open-system dynamics changes algorithm behavior and enables strategies that leverage decoherence rather than only mitigating it. It summarizes progress toward quantum speedups across key QML primitives while emphasizing that real devices operate at nonzero temperature, making coherence loss a central performance factor.","[www.advquantumtech.com](www.advquantumtech.com)  \nBenefits of Open Quantum Systems for Quantum Machine Learning  \nMaría Laura Olivera-Atencio, Lucas Lamata, and Jesús Casado-Pascual*  \nQuantum machine learning (QML) is a discipline that holds the promise of revolutionizing data processing and problem-solving. However, dissipation and noise arising from the coupling with the environment are commonly perceived as major obstacles to its practical exploitation, as they impact the coherence and performance of the utilized quantum devices. Significantefforts have been dedicated to mitigating and controlling their negative effectson these devices. This perspective takes a different approach, aiming to harness the potential of noise and dissipation instead of combating them. Surprisingly, it is shown that these seemingly detrimental factors can provide substantial advantages in the operation of QML algorithms under certain circumstances. Exploring and understanding the implications of adapting QML algorithms to open quantum systems opens up pathways for devising strategies that effectively leverage noise and dissipation. The recent works analyzed in this perspective represent only initial steps toward uncovering other potential hidden benefits that dissipation and noise may offer. As exploration in this field continues, significant discoveries are anticipated that could reshape the future of quantum computing.  \nefficiently. The main aim in this fieldis to accelerate machine learning calculations via employing the speedups produced by genuine quantum properties such as entanglement and superposition. While a definitive demonstration of this capability is yet to be achieved, notable progress is being made in both theoretical and experimental aspects. In particular, some important theoretical results and implementations have been achieved, including, for example, linear solvers of equations,[6] quantum principal component analyses,[7] quantum support vector machines,[8] quantum annealers,[9] variational quantum eigensolvers,[10] quantum Boltzmann machines,[11,12] quantum reinforcement learning (QRL),[13–21] quantum memristors,[22] quantum feature spacesand kernels,[23] and quantum generative adversarial networks.[24] Some of these have speedups relying on the quantum phase estimation algorithm, others are based on  \n1. Introduction  \nQuantum machine learning[1–5] (QML) is a rapidly advancing field within quantum technologies, aiming to leverage quantum devices to perform machine learning computations more  \nM. L. Olivera-Atencio, J. Casado-Pascual Física Teórica  \nUniversidad de Sevilla  \nApartado de Correos 1065, Sevilla 41080, Spain E-mail: [jcasado@us.es](jcasado@us.es)  \nL. Lamata  \nDepartamento de Física Atómica, Molecular y Nuclear Universidad de Sevilla  \nSevilla 41080, Spain  \nL. Lamata  \nInstituto Carlos I de Física Teórica y Computacional Universidad de Granada  \nGranada 18071, Spain  \nThe ORCID identification number(s) for the author(s) of this article can be found under [https://doi.org/10.1002/qute.202300247](https://doi.org/10.1002/qute.202300247)  \n© 2023 The Authors. Advanced Quantum Technologies published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \nDOI: 10.1002/qute.202300247  \nGrover search, and others obtain heuristic gains when resources are limited. Even if it is hard to rigorously prove a quantum speedup with respect to any classical machine learning protocol, there is hope inside the QML community that this may be oneof the areas inside quantum technologies that may have useful applications in industry and society in the nearer time.  \nSome of the advantages of using quantum systems for machine learning tasks arise from the fact that quantum mechanics is well described by linear algebra, which is a common framework in machine learning protocols, at leas","cbCaiag9UDpixI4Z","https://ap.wps.com/l/cbCaiag9UDpixI4Z","pdf",315070,1,9,"English","en",105,"# Introduction\n## Quantum speedups and QML primitives\n## Noise, dissipation, and decoherence in real devices\n## Motivation for open-system perspective","[{\"question\":\"Why are noise and dissipation important for quantum machine learning?\",\"answer\":\"Noise and dissipation come from coupling to the environment and affect coherence and performance. Instead of only treating them as drawbacks, the perspective shows they can provide advantages under certain conditions.\"},{\"question\":\"What problem does decoherence cause for quantum systems used in QML?\",\"answer\":\"Decoherence is identified as the primary cause of faulty behavior in controllable quantum systems. Since QML targets implementation on devices, coherence loss must be incorporated when developing and evaluating algorithms.\"},{\"question\":\"How does the document suggest QML algorithms should be adapted?\",\"answer\":\"It argues for incorporating open quantum system considerations so algorithms can account for noise and decoherence. This enables strategies that effectively leverage dissipation and noise rather than ignoring them.\"}]","Benefits of Open Quantum Systems for Quantum Machine Learning | PDF",1785901501,23,{"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},"benefits-of-open-quantum-systems-for-quantum-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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/benefits-of-open-quantum-systems-for-quantum-machine-learning/125837/",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","2026-08-05",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},"Why are noise and dissipation important for quantum machine learning?","Question",{"text":75,"@type":76},"Noise and dissipation come from coupling to the environment and affect coherence and performance. Instead of only treating them as drawbacks, the perspective shows they can provide advantages under certain conditions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem does decoherence cause for quantum systems used in QML?",{"text":80,"@type":76},"Decoherence is identified as the primary cause of faulty behavior in controllable quantum systems. Since QML targets implementation on devices, coherence loss must be incorporated when developing and evaluating algorithms.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the document suggest QML algorithms should be adapted?",{"text":84,"@type":76},"It argues for incorporating open quantum system considerations so algorithms can account for noise and decoherence. 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