A Modified Q-Learning Algorithm for Control of Two-Qubit Systems

Abstract

This paper investigates quantum control problems using tabular Q-learning. A modified tabular Q-learning algorithm based on dynamic greedy method is proposed and the proposed algorithm succeeds for finding control sequences to drive a two-qubit system to a given target state with high fidelity. The modified algorithm also shows improved performance over the traditional Q-learning for solving quantum control problems on continuous states space. Moreover, the modified tabular Q-learning algorithm is compared with stochastic gradient descent and Krotov algorithms for solving quantum control problems. Simulation results on a two-qubit system demonstrate the effectiveness of the proposed algorithm.