Gong, Zhe-XuanLidiak, Alexander G.2022-11-232022-11-232022https://hdl.handle.net/11124/15524Includes bibliographical references.2022 Summer.The understanding of quantum many-body systems is at the core of various quantum technologies that could revolutionize our society, including quantum computing and quantum simulation in particular. This thesis focuses on practical applications of machine learning to the simulation, control, and understanding of quantum manybody systems. First, variational learning using artificial neural networks is leveraged to achieve efficient classical simulation of quantum many-body systems and is further developed to allow GPU accelerated computing. Second, a machine learning based quantum optimal control algorithm is developed to design speed-optimized quantum gates for a superconducting qubit based quantum computer. Its advantage over conventional algorithms is demonstrated both theoretically and experimentally. Third, unsupervised machine learning is applied to identify complex quantum phase transitions. A specific method known as diffusion map is shown to be capable of learning a wide range of non-trivial quantum phases and is applicable to state-of-art quantum simulation experiments. Finally, a new quantum state tomography protocol based on supervised machine learning is developed, which outperforms many existing protocols especially for states generated by one-dimensional noisy quantum computers. Each of these achievements is integral to the development of quantum technologies in realistic settings.born digitaldoctoral dissertationsengCopyright of the original work is retained by the author.autoregressive artificial neural network variational ground-state searchmachine learningmatrix product operator supervised quantum state tomographynonlinear unsupervised learningquantum many-body physicsquantum optimal control - pulse optimization for fast 2-qubit gatesText2022-11-05