Deep Reinforcement Learning With Curriculum Design for Quantum State Classification

Abstract

In quantum information science, one of the ambitious goals is to look for an efficient technique for classifying multiple quantum states. To solve the binary classification problem for multiple quantum states characterized by parameters, we propose a deep reinforcement learning with curriculum design (DRL-CD) method. In DRL-CD, a series of tasks are created, using state parameter intervals and fidelity thresholds, to form a curriculum. Then, a quantum state binary classifier can be obtained by utilizing deep reinforcement learning (DRL) to solve each task in the designed curriculum. In particular, we construct a training set by sampling the state parameter interval corresponding to each task, and each task is accomplished by learning the control strategies capable of steering the sampled quantum states to the target state. In addition, a knowledge review method is proposed to prevent DRL from forgetting the learned classification knowledge. Some state classification problems of the spin-1/2 quantum system and $\Lambda$-type atomic system are solved by the proposed DRL-CD method, and comparison experiments with deep Q-network (DQN) and stochastic gradient descent (SGD) show the better classification performance of DRL-CD.