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• W3101119258 abstract "The ability to prepare a physical system in a desired quantum state is central to many areas of physics such as nuclear magnetic resonance, cold atoms, and quantum computing. Yet, preparing states quickly and with high fidelity remains a formidable challenge. In this work we implement cutting-edge Reinforcement Learning (RL) techniques and show that their performance is comparable to optimal control methods in the task of finding short, high-fidelity driving protocol from an initial to a target state in non-integrable many-body quantum systems of interacting qubits. RL methods learn about the underlying physical system solely through a single scalar reward (the fidelity of the resulting state) calculated from numerical simulations of the physical system. We further show that quantum state manipulation, viewed as an optimization problem, exhibits a spin-glass-like phase transition in the space of protocols as a function of the protocol duration. Our RL-aided approach helps identify variational protocols with nearly optimal fidelity, even in the glassy phase, where optimal state manipulation is exponentially hard. This study highlights the potential usefulness of RL for applications in out-of-equilibrium quantum physics." @default.
• W3101119258 created "2020-11-23" @default.
• W3101119258 creator A5015366699 @default.
• W3101119258 creator A5025748256 @default.
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• W3101119258 creator A5037693685 @default.
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• W3101119258 date "2018-09-27" @default.
• W3101119258 modified "2023-10-17" @default.
• W3101119258 title "Reinforcement Learning in Different Phases of Quantum Control" @default.
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• W3101119258 doi "https://doi.org/10.1103/physrevx.8.031086" @default.
• W3101119258 hasPublicationYear "2018" @default.
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