[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127024-en":3,"doc-seo-127024-105":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},127024,2336474466412,"Ezra","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","An introduction to Bayesian simulation-based inference for quantum machine learning with examples","Simulation-based inference (SBI) addresses how to infer simulator parameters in a Bayesian framework to quantify epistemic uncertainty. This work studies Bayesian SBI that uses a parameterized quantum circuit (PQC) as the underlying simulator, aligning the approach with the idea that quantum computers are suited for simulating certain physical phenomena. The article extends quantum machine learning beyond likelihood-based training by handling the likelihood-free nature of PQC learning, validated through experimental results on two tasks.","An introduction to Bayesian simulation-based inference for quantum machine learning with examples  \nCitation for published version (APA):  \nNikoloska, I. , & Simeone, O. (2024) . An introduction to Bayesian simulation-based inference for quantum machine learning with examples. Frontiers in Quantum Science and Technology , 3, Article 1394533. [https://doi.org/10.3389/frqst.2024.1394533](https://doi.org/10.3389/frqst.2024.1394533)  \nDOI:  \n10.3389/frqst.2024.1394533  \nDocument status and date:  \nPublished: 29/08/2024  \nDocument Version:  \nPublisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)  \nPlease check the document version of this publication:  \n• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.  \n• The final author version and the galley proof are versions of the publication after peer review.  \n• The final published version features the final layout of the paper including the volume, issue and page numbers.  \nLink to publication  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal.  \nIf the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement:  \n[www.tue.nl/taverne](www.tue.nl/taverne)  \nTake down policy  \nIf you believe that this document breaches copyright please contact us at:  \n[openaccess@tue.nl](openaccess@tue.nl)  \nproviding details and we will investigate your claim.  \nDownload date: 23. Oct. 2024  \nTYPE Original Research PUBLISHED 29 August 2024  \nDOI 10.3389/frqst.2024.1394533  \nOPEN ACCESS  \nEDITED BY  \nJulio De Vicente,  \nUniversidad Carlos III de Madrid, Spain  \nREVIEWED BY  \nGabriel Nathan Perdue,  \nFermilab Accelerator Complex, Fermi National Accelerator Laboratory (DOE), United States Laszlo Gyongyosi,  \nBudapest University of Technology and Economics, Hungary  \n*CORRESPONDENCE  \nIvana Nikoloska,  [i.nikoloska@tue.nl](i.nikoloska@tue.nl)  \nRECEIVED 01 March 2024  \nACCEPTED 05 August 2024  \nPUBLISHED 29 August 2024  \nCITATION  \nNikoloska I and Simeone O (2024) An introduction to Bayesian simulation-based inference for quantum machine learning with examples.  \nFront. Quantum Sci. Technol. 3:1394533 .  \ndoi: 10.3389/frqst.2024.1394533  \nCOPYRIGHT  \n© 2024 Nikoloska and Simeone. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nAn introduction to Bayesian simulation-based inference for quantum machine learning with examples  \nIvana Nikoloska 1* and Osvaldo Simeone 2  \n1Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands, 2Department of Engineering, King ’s College London, London, United Kingdom  \nSimulation is an indispensable tool in both eng","cbCainyhk7AhEp0V","https://ap.wps.com/l/cbCainyhk7AhEp0V","pdf",1112849,1,11,"English","en",105,"# Introduction\n## Context and motivation","[{\"question\":\"What problem does simulation-based inference (SBI) address in Bayesian settings?\",\"answer\":\"SBI focuses on optimizing a parametric simulator’s parameters and, in Bayesian formulations, quantifying epistemic uncertainty about those parameters.\"},{\"question\":\"How does the paper use a parameterized quantum circuit in SBI?\",\"answer\":\"It leverages a parameterized quantum circuit (PQC) as the underlying simulator inside a Bayesian SBI framework.\"},{\"question\":\"Why does the work move beyond likelihood-based methods for PQC training?\",\"answer\":\"Because PQC training is described as likelihood-free, the proposed approach is designed to account for that property rather than relying on likelihood-based strategies.\"}]","An introduction to Bayesian simulation-based inference for quantum machine learning with examples | 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