Reinforcement Learning in Different Phases of Quantum Control

被引:291
作者
Bukov, Marin [1 ]
Day, Alexandre G. R. [1 ]
Sels, Dries [1 ,2 ]
Weinberg, Phillip [1 ]
Polkovnikov, Anatoli [1 ]
Mehta, Pankaj [1 ]
机构
[1] Boston Univ, Dept Phys, 590 Commonwealth Ave, Boston, MA 02215 USA
[2] Univ Antwerp, Theory Quantum & Complex Syst, B-2610 Antwerp, Belgium
来源
PHYSICAL REVIEW X | 2018年 / 8卷 / 03期
基金
美国国家科学基金会; 加拿大自然科学与工程研究理事会;
关键词
D O I
10.1103/PhysRevX.8.031086
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
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 nonintegrable 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.
引用
收藏
页数:15
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