Deep Reinforcement Learning for Control Design of Quantum Gates

This paper investigates quantum gate control problems using the deep reinforcement learning algorithm, i.e., a model-free machine learning method. We implement the twin delayed deep deterministic policy gradient (TD3) algorithm to search for piece-wise constant control pulses for quantum gates throu...

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Bibliographic Details
Main Authors: Hu, Shouliang, Chen, Chunlin, Dong, Daoyi
Format: Conference Proceeding
Language:English
Subjects:
Deep reinforcement learning
Evolutionary computation
Machine learning algorithms
Pipelines
quantum control
quantum gate
Reinforcement learning
Simulation
Training
Transfer learning
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Summary:This paper investigates quantum gate control problems using the deep reinforcement learning algorithm, i.e., a model-free machine learning method. We implement the twin delayed deep deterministic policy gradient (TD3) algorithm to search for piece-wise constant control pulses for quantum gates through the trail interaction with the quantum system. Simulation results on four typical gates, including three one-qubit gates and a two-qubit CNOT gate, demonstrate that deep reinforcement learning exhibits improved performance for quantum gate control tasks. By setting punishment for steps in the reward function, DRL can automatically find a shorter control sequence than the traditional gradient-based algorithm (e.g., GRAPE algorithm) and the evolutionary algorithm (e.g., DE algorithm) while maintaining high control precision.
ISSN:2770-8373
DOI:10.23919/ASCC56756.2022.9828135