[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125849-en":3,"doc-seo-125849-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125849,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Benefits of Open Quantum Systems for Quantum Machine Learning","Quantum machine learning (QML) promises major advances in data processing, yet dissipation and environmental noise are often treated as barriers because they degrade coherence and device performance. This perspective reframes the problem by leveraging noise and dissipation as potential resources rather than only nuisances. It surveys recent research showing how adapting QML algorithms to open quantum systems can reveal operational advantages under specific conditions. The discussion highlights remaining early-stage insights and points toward future strategies.","[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","cbCaibvt9hdmhO5W","https://ap.wps.com/l/cbCaibvt9hdmhO5W","pdf",315119,6,1,9,"English","en",105,"# Introduction\n## Motivation: QML speedups and practical obstacles\n## Environment effects: dissipation, noise, and decoherence\n## Reframing noise and dissipation as resources\n## Survey scope and outlook","[{\"question\":\"Why are dissipation and noise considered obstacles in QML?\",\"answer\":\"They arise from coupling to the environment and can degrade coherence, leading to faulty behavior and reduced performance in quantum devices.\"},{\"question\":\"What is the main perspective of the document on noise and dissipation?\",\"answer\":\"It aims to harness the potential of noise and dissipation instead of only combating their negative effects, showing advantages can occur in certain scenarios.\"},{\"question\":\"How does studying open quantum systems change the evaluation of QML algorithms?\",\"answer\":\"It incorporates environmental impacts, such as decoherence and coherence loss, which many existing purely mathematical QML algorithms often neglect when predicting real-device performance.\"}]","Benefits of Open Quantum Systems for Quantum Machine Learning | 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are dissipation and noise considered obstacles in QML?","Question",{"text":77,"@type":78},"They arise from coupling to the environment and can degrade coherence, leading to faulty behavior and reduced performance in quantum devices.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What is the main perspective of the document on noise and dissipation?",{"text":82,"@type":78},"It aims to harness the potential of noise and dissipation instead of only combating their negative effects, showing advantages can occur in certain scenarios.",{"name":84,"@type":75,"acceptedAnswer":85},"How does studying open quantum systems change the evaluation of QML algorithms?",{"text":86,"@type":78},"It incorporates environmental impacts, such as decoherence and coherence loss, which many existing purely mathematical QML algorithms often neglect when predicting real-device 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