控制理论与应用2024，Vol.41Issue(12)：2277-2285,9.DOI:10.7641/CTA.2023.30007
基于熵和不等概率的量子强化学习控制
Quantum reinforcement learning control based on entropy and unequal probability
摘要
Abstract
High-precision control of complicated quantum systems is one of the key technologies for realizing quantum computing and quantum information processing.Deep reinforcement learning algorithms have been applied to quantum control problems to design optimal strategies for various quantum systems.In order to achieve rapid and accurate quantum state preparation,a deep reinforcement learning algorithm based on entropy and unequal probability is proposed,where action selection strategy is improved by introducing the notion of entropy from information theory.The entropy value of the current state is obtained through its action value and"exploration"or"exploitation"is determined based on the entropy value,where the unequal probability is employed to randomly select actions for"exploitation".The agent in the proposed reinforcement learning algorithm focuses on exploitation for sufficiently learned states and on exploration for non-sufficiently learned states,until the task is accomplished.Numerical simulation results on qubit systems show that the proposed algorithm achieves the preparation of eigenstates and entangled states with faster convergence speed and higher fidelities with respect to the conventional reinforcement learning algorithms.关键词
强化学习/动作选择策略/熵/不等概率/量子态制备Key words
reinforcement learning/action selection strategy/entropy/unequal probability/quantum state preparation引用本文复制引用
张玉瑶,匡森..基于熵和不等概率的量子强化学习控制[J].控制理论与应用,2024,41(12):2277-2285,9.基金项目
国家自然科学基金项目(62373342)资助.Supported by the National Natural Science Foundation of China(62373342). （62373342）
