[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121044-en":3,"doc-seo-121044-105":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},121044,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Machine Learning assisted Quantum Error Correction and Quantum Feedback Control - Inaugural-Dissertation","Quantum feedback control manipulates quantum systems toward specific goals using continuous observation, making it a key element for future quantum technologies. Fault-tolerant quantum computing motivates quantum error correction, yet control design is difficult because incomplete system descriptions and external noise introduce uncertainties. This thesis investigates machine-learning approaches to learn quantum feedback control strategies, demonstrating near-optimal robustness. It presents hierarchical decoding for topological surface codes with near-linear scaling and reinforcement-learning benchmarks for nonlinear quantum cartpole control.","Machine Learning assisted Quantum Error Correction and Quantum Feedback Control  \nInaugural-Dissertation  \nzur  \nErlangung des Doktorgrades der Mathematisch-Naturwissenschaftlichen Fakultät der Universität zu Köln  \nvorgelegt von  \nKai Meinerz  \naus Wuppertal  \nKöln 2023  \nBerichterstatter: Prof. Dr. Simon Trebst  \nProf. Dr. David Gross  \nTag der mündlichen Prüfung: 15.03.2024  \nAbstract  \nQuantum feedback control is a field of research that deals with the manipulation of quantum systems towards a goal, based on continuous observation of the systems. These control strategies have been identified as an essential part of the development of future quantum technologies. An example of this is the formulation of quantum error correction strategies for fault-tolerant quantum computing. The design of control strategies is a challenging task, however, as incomplete descriptions of the quantum system at hand and external noise factors often introduce uncertainties into the system, leading to performance degradation. In this thesis, we focus on the use of machine learning based approaches to find quantum control feedback strategies. These approaches have demonstrated the ability to find near-optimal and robust strategies.  \nIn a first study, we develop a control strategy for quantum error correction on topological surface codes in the form of a hierarchical decoder. Using a combination of machine learning and combinational decoding, we were able to demonstrate scalable decoding. We achieved nearly linear-time scaling, while still maintaining a high precision decoding comparable to state-of-theart conventional decoding strategies. The robustness of the decoding strategies is demonstrated through performing tests on different error models including depolarizing noise and faulty syndrome measurements, as well as by changing the underlying error correction code to the rotated surface code. Analyzing the correction applied by the hierarchical decoder provides us with insight into the learned strategies and identifies possible strengths and weaknesses of the decoder, allowing for possible further developments of machine learning and algorithmic decoders based on these findings.  \nIn a second study, we investigate the use of reinforcement learning for quantum feedback control. Since reinforcement learning encompasses model-free approaches, it is a prime candidate for finding robust strategies that are not hindered by unknown uncertainties in the experimental system. A well-known problem of reinforcement learning is the sometimes unstable training, caused by the encountered “exploration versus exploitation” dilemma during the process. Therefore, we have implemented a toy model, the quantum cartpole, designed to serve as a benchmark environment for the development of reinforcement learning based quantum feedback strategies. Based on weak measurements, the implemented control strategies have to deal with partial observability and measurement induced feedback. We provide benchmarks for three different variations of the system, including linear and nonlinear systems, using the classical control theory algorithm, linear quadratic Gaussian control, and a reinforcement learning based control strategy. By examining these results, we show the importance of state estimation techniques as part of the control process, and demonstrate the ability of reinforcement to find novel strategies that outperform conventional control strategies in highly nonlinear systems.  \nContents  \nOutline 9  \n1 Introduction 11  \n1.1 Quantum control models ............................... 11  \n1.2 Control strategies ................................... 13  \n1.3 Promises of fault-tolerant computing ........................ 15  \n1.4 Quantum control application in quantum computing ............... 16  \n1.5 Noisy intermediate scale ............................... 19  \n2 Machine learning 21  \n2. 1 Artificial neural networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22  ","cbCaic98D2dAyS74","https://ap.wps.com/l/cbCaic98D2dAyS74","pdf",3872893,1,148,"English","en",105,"# Contents\n## Outline\n## 1 Introduction\n## 2 Machine learning\n## 3 Quantum error correcting codes\n## 4 Error correction on topological surface codes","[{\"question\":\"What is the central research focus of the thesis?\",\"answer\":\"The thesis focuses on using machine learning to find quantum control feedback strategies for tasks such as quantum error correction and quantum feedback control under uncertainty.\"},{\"question\":\"How does the first study perform quantum error correction?\",\"answer\":\"It develops a hierarchical decoder for topological surface codes by combining machine learning with combinational decoding, achieving nearly linear-time scaling while preserving high decoding precision.\"},{\"question\":\"Why is reinforcement learning considered suitable for quantum feedback control?\",\"answer\":\"Reinforcement learning is model-free, which helps it find robust strategies despite unknown uncertainties in experimental systems, and the thesis benchmarks approaches on a quantum cartpole toy model.\"}]","Machine Learning assisted Quantum Error Correction and Quantum Feedback Control - 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