[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117384-en":3,"doc-seo-117384-105":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},117384,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Interplay between Quantum Computation and Machine Learning - Dissertation","Quantum errors prevent quantum computers from outperforming classical supercomputers, despite rapid progress in quantum error correction and quantum error mitigation. Machine learning, widely used for pattern recognition, provides new strategies to improve these methods against quantum noise. In parallel, quantum errors shape broader quantum computing applications, especially quantum machine learning that uses quantum resources to seek advantage. This dissertation studies both directions: using machine learning to enhance error correction and mitigation on quantum circuits, and applying quantum computation to improve robustness of quantum learning models under worst-case errors and decoherence.","UC Berkeley  \nUC Berkeley Electronic Theses and Dissertations  \nTitle  \nInterplay between Quantum Computation and Machine Learning  \nPermalink  \n[https://escholarship.org/uc/item/8w49w7hc](https://escholarship.org/uc/item/8w49w7hc)  \nAuthor  \nLiao, Haoran  \nPublication Date  \n2023  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nInterplay between Quantum Computation and Machine Learning  \nby  \nHaoran Liao  \nA dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy  \nin  \nPhysics  \nin the  \nGraduate Division  \nof the  \nUniversity of California, Berkeley  \nCommittee in charge:  \nProfessor K. Birgitta Whaley, Co-chair Professor Irfan Siddiqi, Co-chair Professor Hartmut Häffner Professor Uroš Seljak  \nFall 2023  \nInterplay between Quantum Computation and Machine Learning  \nCopyright 2023  \nby  \nHaoran Liao  \n1  \nAbstract  \nInterplay between Quantum Computation and Machine Learning  \nby  \nHaoran Liao  \nDoctor of Philosophy in Physics  \nUniversity of California, Berkeley  \nProfessor K. Birgitta Whaley, Co-chair  \nProfessor Irfan Siddiqi, Co-chair  \nQuantum errors remain the primary barrier inhibiting quantum computers from outperforming classical supercomputers. To overcome this challenge, a diverse array of strategies has been developed, encompassing quantum error correction and quantum error mitigation. Machine learning, maturing as a widely adopted approach for pattern recognition, offers new perspectives in enhancing the aforementioned strategies to tackle quantum errors. Furthermore, the implications of quantum errors extend to various applications of quantum computing, notably in quantum machine learning which leverages quantum resources for potential advantage over classical counterparts. This dissertation delves into these intertwined parts, examining the interplay between quantum computation and machine learning. The first part concerns machine learning for enhancing quantum computations. It addresses challenges in correcting errors that occurred to continuously measured logical states, and in improving the efficiency in mitigating errors on both small-and large-scale quantum circuits for increased accuracies in the targeted expectation values, serving as an example of using classical machine learning on quantum data. The second part of this dissertation explores quantum computation for machine learning. It provides theoretical and numerical analysis on the robustness of quantum machine learning models against worst-case errors on input encoded quantum states received through quantum communication, or against quantum decoherence during model training and evaluation, serving as an example of applying quantum machine learning on classical data.  \ni  \nTo my parents  \nii  \nContents  \nContents ii  \nList of Figures v  \nList of Tables xiii  \nI Machine Learning for Quantum Information Processing 1  \n1 Continuous Quantum Error Correction on Superconducting Qubits 2  \n1.1 Background on Quantum Error Correction ..................... 2  \nQuantum Error ...................................... 3  \nDecoherence – Relaxation and Dephasing ...................... 5  \nQuantum Error Correcting Code ........................... 7  \n1.2 Background on Continuous Quantum Error Correction .............. 8  \nContinuous Measurement ................................ 9  \nMaster Equations ..................................... 10  \nHomodyne Measurements ................................ 12  \n1.3 Continuous Quantum Error Correction on Small Stabilizer Code ........ 14  \nResonator Transients .................................. 19  \nImpact of Auto-correlations .............................. 20  \n1.4 Bayesian Inference and Machine Learning ...................... 23  \nDiscrete Bayesian Classifier ............................... 25  \nRecurrent Neural Network ............................... 28  \n1.5 Simulated Experiments ..","cbCaid2G5EcupvaT","https://ap.wps.com/l/cbCaid2G5EcupvaT","pdf",4097892,1,154,"English","en",105,"# Contents\n## Machine Learning for Quantum Information Processing\n## Quantum Machine Learning","[{\"question\":\"Why are quantum errors central to this dissertation’s motivation?\",\"answer\":\"Quantum errors are identified as the main barrier preventing quantum computers from achieving better performance than classical supercomputers.\"},{\"question\":\"How does the dissertation use machine learning to support quantum computations?\",\"answer\":\"It develops machine-learning approaches to correct errors in continuously measured logical states and to improve the efficiency of error mitigation on small and large quantum circuits for more accurate expectation values.\"},{\"question\":\"What aspects of quantum machine learning are analyzed in the second part?\",\"answer\":\"It studies quantum computation as a way to make quantum machine learning models more robust against worst-case errors in quantum communication–encoded inputs and against decoherence during model training and evaluation.\"}]","Interplay between Quantum Computation and Machine Learning - 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