Quantum reinforcement learning control based on entropy and unequal probability
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.
reinforcement learningaction selection strategyentropyunequal probabilityquantum state preparation
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