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arXiv:2310.03036 · Soft Computing

A quantum system control method based on enhanced reinforcement learning

Wenjie Liu, Bosi Wang, Jihao Fan, Y. F. Ge, Mohammed Zidan

Published Jun 2, 2022• 26 citations
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Abstract

Abstract The design of quantum system control is a key task to a powerful quantum information technology. In practical, 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. So a quantum system control method based on enhanced reinforcement learning (QSC-ERL) is proposed. A satisfactory control strategy is obtained through enhanced reinforcement learning so that the quantum system can be evolved accurately from the initial state to the 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 can achieve high fidelity learning control of quantum systems and improve the efficiency of quantum system control.

DOI
10.1007/s00500-022-07179-5

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Abstract The design of quantum system control is a key task to a powerful quantum information technology. In practical, 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. So a quantum system control method based on enhanced reinforcement learning (QSC-ERL) is proposed. A satisfactory control strategy is obtained through enhanced reinforcement learning so that the quantum system can be evolved accurately from the initial state to the 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 can achieve high fidelity learning control of quantum systems and improve the efficiency of quantum system control.
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In recent years, some experimental studies and simulations show that reinforcement learning (RL) is an effective learning control approach for solving certain quantum control problems. In this paper, Q-learning with different exploration strategies (e.g., ε-greedy and Softmax), probabilistic Q-learning (PQL) and quantum reinforcement learning (QRL) are applied to solve the state transition problem of two-level quantum systems (e.g., spin-1/2 systems), respectively. These reinforcement learning algorithms are introduced and analyzed regarding the learning control problem of the spin-1/2 system. According to the constraints of the control fields, two typical kinds of controllers, i.e., three-switch controller and Bang-Bang controller, are designed using reinforcement learning. The learning performance of the above RL algorithms for both of the three-switch control and Bang-Bang control of two-level quantum systems are demonstrated and analyzed.
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This paper investigates the application of reinforcement learning (RL) for adaptive quantum control. Traditional quantum control methods often rely on pre-designed pulse shapes optimized for specific scenarios, lacking adaptability to variations in system parameters or environmental noise. This work proposes a novel framework where an RL agent learns to dynamically adjust quantum control pulses in real-time, maximizing the fidelity of quantum operations. The agent receives feedback from the quantum system, typically measured through state variables, and utilizes this information to optimize the control pulse sequence. This adaptive approach addresses the limitations of static control schemes and promises enhanced robustness and performance in complex quantum systems. The core claim centers on utilizing reinforcement learning to design optimal quantum control pulses. The central mechanism involves a trained controller dynamically adjusting pulses based on system feedback. The presented methodology offers a new approach to quantum control, particularly relevant in noisy environments and systems with parameter variations. The research demonstrates the potential of RL for achieving superior quantum control outcomes compared to conventional techniques. ---
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The fast preparation of a quantum state with high fidelity is a key problem for a quantum system. We propose two modified algorithms based on the cutting-edge reinforcement learning methods for the flip from an initial state to a target one in a quantum spin system. The balance between exploration and exploitation and the size of a state space are key factors for reinforcement learning methods. In the first algorithm, we propose a modified ϵ\epsilonϵ-greedy strategy instead of the ε-greedy strategy to balance exploration and exploitation. Furthermore, we use the fidelity of the final state as the reward and leverage piecewise-constant driving protocols in every duration. The results show that the rate of correction of this algorithm is greater than that of Q-learning with ε-greedy strategy. However, it will still cost much time resource if the rotation angle from a state to the next state is too small. Thus, we initialize the state-action value table to reduce the state space in the second modified Q-learning algorithm (MQL), which can be found that the efficiency of learning is greatly improved.