使用深度强化学习的无模型量子门设计和校准
Model-free Quantum Gate Design and Calibration using Deep Reinforcement Learning
摘要：
高保真量子门设计对于量子计算和量子通信等各种量子技术都具有重要意义。在量子系统的动力学模型下，已经提出了许多用于量子门设计的控制策略。然而，量子系统通常对噪声高度敏感，并且对于许多实际应用而言，获得其精确建模可能是困难的。因此，基于量子系统模型的控制策略对于量子门设计可能是不实用的。此外，量子测量使量子态坍缩，这使得在控制过程中通过测量获得信息具有挑战性。在本文中，我们提出了一种新的训练框架，使用深度强化学习进行无模型量子控制。所提出的框架仅依赖于控制过程结束时的测量，并提供了在学习过程中无需访问量子系统即可找到最优控制策略的能力。数值模拟表明，所提出的技术的有效性无模型量子门设计和量子门校准使用离政策强化学习算法。
英文摘要：
High-fidelity quantum gate design is important for various quantum technologies, such as quantum computation and quantum communication. Numerous control policies for quantum gate design have been proposed given a dynamical model of the quantum system of interest. However, a quantum system is often highly sensitive to noise, and obtaining its accurate modeling can be difficult for many practical applications. Thus, the control policy based on a quantum system model may be unpractical for quantum gate design. Also, quantum measurements collapse quantum states, which makes it challenging to obtain information through measurements during the control process. In this paper, we propose a novel training framework using deep reinforcement learning for model-free quantum control. The proposed framework relies only on the measurement at the end of the control process and offers the ability to find the optimal control policy without access to quantum systems during the learning process. The effectiveness of the proposed technique is numerically demonstrated for model-free quantum gate design and quantum gate calibration using off-policy reinforcement learning algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
