A quantum system control method based on enhanced reinforcement learning

TL;DR

This paper introduces an enhanced reinforcement learning approach for quantum system control, achieving high fidelity and efficiency in state evolution with limited resources.

Contribution

It proposes a novel quantum control method based on enhanced reinforcement learning and new neural networks, improving learning speed and accuracy.

Findings

Achieves close to 1 fidelity in quantum control tasks.

Requires fewer episodes for quantum state evolution.

Performs well under resource constraints.

Abstract

Traditional quantum system control methods often face different constraints, and are easy to cause both leakage and stochastic control errors under the condition of limited resources. Reinforcement learning has been proved as an efficient way to complete the quantum system control task. To learn a satisfactory control strategy under the condition of limited resources, a quantum system control method based on enhanced reinforcement learning (QSC-ERL) is proposed. The states and actions in reinforcement learning are mapped to quantum states and control operations in quantum systems. By using new enhanced neural networks, reinforcement learning can quickly achieve the maximization of long-term cumulative rewards, and a quantum state can be evolved accurately from an initial state to a target state. According to the number of candidate unitary operations, the three-switch control is used for simulation experiments. Compared with other methods, the QSC-ERL achieves close to 1 fidelity learning control of quantum systems, and takes fewer episodes to quantum state evolution under the condition of limited resources.