[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127738-en":3,"doc-seo-127738-105":30,"detail-sidebar-cat-0-en-105":92},{"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":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},127738,962084926284,"Aurora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","A machine learning ride in the physics theme park - from quantum to biophysics - doctoral thesis","Artificial intelligence integration accelerates progress and discoveries across scientific research by enabling models that process extensive datasets, guide exploration and hypothesis formation, improve experimental design, and support autonomous discovery. This doctoral thesis applies machine learning to hard-to-characterize non-deterministic systems. It uses reinforcement learning for quantum systems and quantum technology tasks, including simplifying complex problems and enabling robust quantum computer calibration. It also develops algorithms to extract time-varying parameters from stochastic trajectories for diffusion, demonstrated in physical and biological experimental settings and adapted to predict e-commerce purchase likelihood.","ICFO  \nTHE INSTITUTE OF PHOTONIC SCIENCES  \nDOCTORAL THESIS  \nA machine learning ride in the physics theme park: from quantum to biophysics  \nAuthor:  \nBorja REQUENA  \nSupervisors:  \nProf. Dr. Maciej LEWENSTEIN Dr. Gorka MUÑOZ-GIL  \nMarch 2024  \niii  \nAbstract  \nThe integration of artificial intelligence into research is propelling progress and discoveries across the entire scientific landscape. Artificial intelligence tools boost the development of novel scientific insights and theories by processing extensive datasets, guiding exploration and hypothesis formation, enhancing experimental setups, and even enabling autonomous discovery. In this thesis, we harness the power of machine learning, a sub-field of artificial intelligence, to study non-deterministic systems, which are amongst the hardest to characterize.  \nOn one hand, we address problems inherent to the study of quantum systems and the development of quantum technologies. Quantum physics presents formidable challenges due to the associated exponential complexity with the size of the system at hand, as well as its intrinsic stochastic nature and the presence of intricate correlations between its components. We employ reinforcement learning, a machine learning technique that excels at dealing with vast hypothesis spaces, to address some of these challenges. Notably, reinforcement learning has demonstrated super-human performance in multiple complex games like Go, which present similar characteristics to the problems encountered in the study of quantum physics. We use it to systematically simplify complex common problems in condensed matter and quantum information processing tasks, as well as to implement robust calibration schemes for quantum computers.  \nOn the other hand, we focus on the characterization of complex stochastic processes, such as diffusion. Understanding diffusion processes is crucial to unravel the complex underlying physical and biological mechanisms governing them. This involves extracting meaningful parameters from the analysis of stochastic trajectories described by tracked particles. However, accurately capturing and analyzing the trajectories presents multiple challenges, stemming from the combination of their random nature, complex dynamics, and experimental drawbacks, such as noise. We develop machine learning algorithms to accurately extract such parameters, even when they vary with time, and demonstrate their applicability in experimental scenarios. Furthermore, we apply similar techniques to study the diffusion of internet users browsing an e-commerce website, predicting their likelihood to make a purchase before closing the session.  \nv  \nResum  \nLa integració de la intel·ligència artificial a la recerca accelera el progrés cap a nous descobriments en tot l’àmbit científic. Les eines d’intel·ligència artificial contribueixen al desenvolupament de noves teories i coneixements processant grans quantitats de dades, guiant l’exploració i la formulació d’hipòtesis, millorant els experiments i, fins i tot, fent possible descobriments automàtics. En aquesta tesi, aprofitem el poder de l’aprenentatge automàtic (“machine learning”), un camp dela intel·ligència artificial, per estudiar sistemes no-deterministes, que es troben entreels més difícils de caracteritzar.  \nPer una banda, tractem problemes inherents a l’estudi de sistemes quàntics idel desenvolupament de noves tecnologies quàntiques. La física quàntica plantejareptes formidables derivats de la complexitat exponencial amb la mida del sistema considerat, en combinació amb una naturalesa intrínsicament estocàstica i la presència de correlacions complexes entre elements del sistema. Per tractar alguns d’aquests reptes, fem servir aprenentatge de reforç (“reinforcement leraning”), una tècnica del’aprenentatge automàtic capaç d’explorar grans espais d’hipòtesis. Per exemple, empleant aquestes tècniques, s’ha aconseguit superar als millors jugadors del mónen jocs complexes com el Go, que presente","cbCaia1bkUsMx9hp","https://ap.wps.com/l/cbCaia1bkUsMx9hp","pdf",6503392,1,174,"English","en",105,"# Abstract\n## Reinforcement learning for quantum systems\n## Quantum technology calibration\n## Learning diffusion from stochastic trajectories\n## Applications: experiments and e-commerce prediction","[{\"question\":\"How does the thesis use machine learning in scientific research?\",\"answer\":\"It leverages machine learning to analyze non-deterministic systems, extract meaningful parameters from large datasets, and support exploration and hypothesis formation relevant to experimental work.\"},{\"question\":\"What role does reinforcement learning play in the quantum physics part?\",\"answer\":\"Reinforcement learning is used to handle the challenges of quantum systems, including simplifying complex problems in condensed matter and quantum information tasks and implementing robust calibration schemes for quantum computers.\"},{\"question\":\"How does the thesis characterize diffusion and other stochastic processes?\",\"answer\":\"It develops machine learning algorithms to extract parameters from stochastic trajectories, including cases where parameters vary with time, and demonstrates applicability in experimental scenarios; it also adapts similar ideas to predict online purchase likelihood before a session ends.\"}]","A machine learning ride in the physics theme park - from quantum to biophysics - doctoral thesis | PDF",1785941334,438,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"a-machine-learning-ride-in-the-physics-theme-park-from-quantum-to-biophysics-doctoral-thesis","",{"@graph":36,"@context":86},[37,54,69],{"@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/a-machine-learning-ride-in-the-physics-theme-park-from-quantum-to-biophysics-doctoral-thesis/127738/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"How does the thesis use machine learning in scientific research?","Question",{"text":76,"@type":77},"It leverages machine learning to analyze non-deterministic systems, extract meaningful parameters from large datasets, and support exploration and hypothesis formation relevant to experimental work.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What role does reinforcement learning play in the quantum physics part?",{"text":81,"@type":77},"Reinforcement learning is used to handle the challenges of quantum systems, including simplifying complex problems in condensed matter and quantum information tasks and implementing robust calibration schemes for quantum computers.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the thesis characterize diffusion and other stochastic processes?",{"text":85,"@type":77},"It develops machine learning algorithms to extract parameters from stochastic trajectories, including cases where parameters vary with time, and demonstrates applicability in experimental scenarios; 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