[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124250-en":3,"doc-seo-124250-105":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},124250,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning in Quantum Mechanical and Optical Systems - Doctoral Dissertation","Machine learning (ML) is increasingly important across science and industry, while quantum computing promises to revolutionize computation. This thesis investigates applying ML to quantum and optical systems, presenting two core contributions: a quantum recurrent neural network (QRNN) architecture and reinforcement learning (RL) for experimental fiber coupling. It introduces a fully quantum recurrent design for qudits, the dissipative quantum recurrent neural network (DQRNN), with quantum and classical training methods. Results show efficient scaling with DQNN width and strong performance on memory-dependent tasks. The study further demonstrates direct RL training on optical experiments, matching expert-level coupling efficiencies within days despite noisy actions and partial observability.","Machine Learning in Quantum Mechanical and Optical Systems  \nVon der Fakult¨at f¨ur Mathematik und Physik der Gottfried Wilhelm Leibniz Universit¨at Hannover  \nzur Erlangung des Grades Doktorin der Naturwissenschaften Dr. rer. nat.  \ngenehmigte Dissertation von  \nM.Sc . Viktoria-Sophie Schmiesing  \n2025  \nReferent: Prof. Dr. Tobias J. Osborne  \nKorreferentin: Jun.-Prof. Dr. Ramona Wolf  \nKorreferent: Prof. Dr. Bodo Rosenhahn  \nTag der Promotion: 16.01.2025  \nii  \nAbstract  \nIn recent years, machine learning (ML) has become increasingly important across science, industry, and daily life. Simultaneously, quantum computing has emerged as a field with the potential to revolutionize computation. This thesis explores the application of ML to quantum and optical systems. We present two main results: the proposal of a quantum recurrent neural network (QRNN) architecture and the use of reinforcement learning (RL) for experimental fiber coupling, a common task in quantum labs.  \nWhen data is quantum in nature, quantum ML techniques may be better suited than classical approaches. One such technique is the feed-forward dissipative quantum neural network (DQNN), which learns general quantum channels from independent and identically distributed data. However, many quantum tasks involve sequential data, such as learning quantum state evolution under time-dependent Hamiltonians or interacting with quantum environments. In classical ML, such tasks can, e.g., be handled by recurrent neural networks (RNNs) . This thesis proposes a fully quantum RNN structure designed for qudits, named dissipative quantum recurrent neural network (DQRNN) . Extending the DQNN framework to the recurrent case, DQRNNs can approximate general causal quantum automata. We present both quantum and classical training algorithms for DQRNNs, showing that the resource requirements scale with the width of the underlying DQNN but not with its depth. Numerical results show that DQRNNs solve memory-dependent tasks beyond the capacity of DQNNs, generalizing well from limited data. One promising future application of DQRNNsis model-based RL in quantum environments.  \nAlthough RL is inherently well-suited for control tasks, RL agents for applications in optical experiments have mostly been trained in simulation. Taking the example of fiber coupling, we show that it is feasible to apply RL directly in experiments. This saves us the time of extensive system and noise modeling. Still, intermediate challenges needed to be overcome such as time-consuming training, noisy actions, and partial observability. For shorter training times, we use a simple virtual testbed for environment tuning and algorithm selection. We demonstrate that an RL agent can learn to overcome noisy actions and partial observability. In four days of training time directly in the experimental setup, using sampleefficient algorithms such as truncated quantile critics (TQC) and soft actor-critic (SAC), it learns to achieve coupling efficiencies comparable to human experts.  \nThis thesis takes key steps toward integrating machine learning in quantum control, introducing DQRNNs that could serve as a powerful tool for modeling quantum environmentsand highlighting the role of RL in experimental physics. By showing how RL can be applied successfully directly in an optical experiment using the example of fiber coupling, this work paves the way for applying RL to more intricate quantum systems.  \nKeywords: Machine learning, quantum computing, recurrent neural networks, reinforcement learning, fiber coupling  \niv  \nAcknowledgments  \nFirst and foremost, I would like to express my deepest gratitude to Tobias J. Osborne, my supervisor, for his unwavering support and insightful guidance throughout this journey. Thank you for introducing me to the fascinating field of machine learning. I am immensely thankful for the numerous fruitful discussions, for your mentorship, and for the safe and open-minded workspace you created.  \nI woul","cbCaipL99BZJNTzs","https://ap.wps.com/l/cbCaipL99BZJNTzs","pdf",7127303,1,206,"English","en",105,"# Abstract\n# Contributions\n## Quantum recurrent neural network (DQRNN)\n## Reinforcement learning for experimental fiber coupling\n# Future directions\n# Keywords","[{\"question\":\"What are the two main results of the thesis?\",\"answer\":\"The thesis proposes a quantum recurrent neural network (QRNN) architecture and applies reinforcement learning (RL) to experimental fiber coupling in quantum labs.\"},{\"question\":\"How does the dissipative quantum recurrent neural network (DQRNN) extend earlier models?\",\"answer\":\"It extends the feed-forward dissipative quantum neural network (DQNN) to a fully recurrent structure for qudits, enabling approximation of general causal quantum automata.\"},{\"question\":\"How is RL applied in the optical fiber coupling experiment, and what challenges are addressed?\",\"answer\":\"RL is trained directly in the experiment rather than only in simulation, avoiding extensive system and noise modeling. The work addresses challenges such as time-consuming training, noisy actions, and partial observability.\"}]","Machine Learning in Quantum Mechanical and Optical Systems - Doctoral Dissertation | PDF",1785821226,519,{"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},"machine-learning-in-quantum-mechanical-and-optical-systems-doctoral-dissertation","",{"@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/machine-learning-in-quantum-mechanical-and-optical-systems-doctoral-dissertation/124250/",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","2026-08-04",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},"What are the two main results of the thesis?","Question",{"text":75,"@type":76},"The thesis proposes a quantum recurrent neural network (QRNN) architecture and applies reinforcement learning (RL) to experimental fiber coupling in quantum labs.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the dissipative quantum recurrent neural network (DQRNN) extend earlier models?",{"text":80,"@type":76},"It extends the feed-forward dissipative quantum neural network (DQNN) to a fully recurrent structure for qudits, enabling approximation of general causal quantum automata.",{"name":82,"@type":73,"acceptedAnswer":83},"How is RL applied in the optical fiber coupling experiment, and what challenges are addressed?",{"text":84,"@type":76},"RL is trained directly in the experiment rather than only in simulation, avoiding extensive system and noise modeling. 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