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arXiv:2007.00838 · Physical Review A

Quantum optimal control of multilevel dissipative quantum systems with reinforcement learning

Zheng An, Hai-Jing Song, Qi-Kai He, D. L. Zhou

Published Jan 11, 2021• 48 citations
arXivHTMLPDF

Abstract

Manipulation and control of the complex quantum system with high precision are essential for achieving universal fault-tolerant quantum computing. For a physical system with restricted control resources, it is a challenge to control the dynamics of the target system efficiently and precisely under disturbances. Here we propose a multilevel dissipative quantum control framework and show that deep reinforcement learning provides an efficient way to identify the optimal strategies with restricted control parameters of the complex quantum system. This framework can be generalized to be applied to other quantum control models. Compared with the traditional optimal control method, this deep reinforcement learning algorithm can realize efficient and precise control for multilevel quantum systems with different types of disturbances.

DOI
10.1103/physreva.103.012404

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Manipulation and control of the complex quantum system with high precision are essential for achieving universal fault-tolerant quantum computing. For a physical system with restricted control resources, it is a challenge to control the dynamics of the target system efficiently and precisely under disturbances. Here we propose a multilevel dissipative quantum control framework and show that deep reinforcement learning provides an efficient way to identify the optimal strategies with restricted control parameters of the complex quantum system. This framework can be generalized to be applied to other quantum control models. Compared with the traditional optimal control method, this deep reinforcement learning algorithm can realize efficient and precise control for multilevel quantum systems with different types of disturbances.
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Abstract Machine learning technology based on artificial neural network has been successfully applied to solve many scientific problems. One of the most interesting areas of machine learning is reinforcement learning, which has natural applicability to optimization problems in physics. In the quantum control task, it is necessary to find a set of optimal control functions to transfer a quantum system from the initial state to the target state with the highest fidelity possible, which is essentially an optimization task. In this paper, we use Deep Deterministic Policy Gradient algorithm (DDPG) to study the quantum control tasks. We use the algorithm to control the transfer of several quantum systems from one state to another. The results show that DDPG algorithm can find a control strategy to make the fidelity of the final state and the target state of the quantum system be maximum value 1. The results show the potential of DDPG in quantum control.
Deep reinforcement learning (DRL) has been recognized as an efficient technique to design optimal strategies for different complex systems without prior knowledge of the control landscape. To achieve a fast and precise control for quantum systems, we propose a novel DRL approach by constructing a curriculum consisting of a set of intermediate tasks defined by fidelity thresholds, where the tasks among a curriculum can be statically determined before the learning process or dynamically generated during the learning process. By transferring knowledge between two successive tasks and sequencing tasks according to their difficulties, the proposed curriculum-based DRL (CDRL) method enables the agent to focus on easy tasks in the early stage, then move onto difficult tasks, and eventually approaches the final task. Numerical comparison with the traditional methods [gradient method (GD), genetic algorithm (GA), and several other DRL methods] demonstrates that CDRL exhibits improved control performance for quantum systems and also provides an efficient way to identify optimal strategies with few control pulses.