A Modified Deep Q-Learning Algorithm for Control of Two-qubit Systems

Abstract

Quantum control refers to the manipulation of dynamical quantum systems to force them to complete given tasks such as preparing a desired state and tracking a designed trajectory. We consider the state preparation problem of a two-qubit closed quantum system from an initial to a desired state. The aim is to achieve a high fidelity in a given fixed time with limited control resources. The Deep Q-learning (DQL) for solving the quantum state preparation problem is explored in this paper. We propose a novel semi-Markov DQL algorithm based on a modified action selection procedure and improved replay memory to enhance performance of standard DQL algorithm. The proposed algorithm shows high performance for discovering high-fidelity control protocols and for converging to a good policy, compared with standard DQL. The proposed modifications enhance the exploration-exploitation ability for DQL agent and the robustness for solving quantum control problem with high-fidelity at different numbers of control steps. Numerical results on a two-qubit closed system show effectiveness of the proposed algorithm. Show more