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Practical applications of machine learning to quantum computing and quantum simulation
Lidiak, Alexander G.
Lidiak, Alexander G.
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2022
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Abstract
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.
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