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arXiv 1902.02157quant-ph
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强化学习何时在量子控制中脱颖而出？状态准备的比较研究

When does reinforcement learning stand out in quantum control? A comparative study on state preparation

Xiao-Ming Zhang, Zezhu Wei, Raza Asad, Xu-Chen Yang, Xin Wang

备注：
8+6 pages, 5+5 figures
发表信息：
npj Quantum Inf. 5:85 (2019)
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摘要：

强化学习已被广泛应用于许多问题，包括量子比特的量子控制。然而，这些问题可以同时通过传统的非机器学习方法来解决，例如随机梯度下降和Krotov算法，并且当控制具有特定约束时，仍然不清楚哪种方法最适合。在这项工作中，我们对三种强化学习算法的有效性进行了比较研究：表格Q学习，深度Q学习和策略梯度，以及两种非机器学习方法：随机梯度下降和Krotov算法，在准备所需的量子态的问题中。我们发现，总体而言，当问题离散化时，深度Q学习和策略梯度算法优于其他算法，例如允许离散控制值，以及当问题扩大时。强化学习算法还可以自适应地降低控制序列的复杂度，缩短运算时间，提高保真度。我们的比较为强化学习在量子控制问题中的适用性提供了见解。

英文摘要：

Reinforcement learning has been widely used in many problems, including quantum control of qubits. However, such problems can, at the same time, be solved by traditional, non-machine-learning methods, such as stochastic gradient descent and Krotov algorithms, and it remains unclear which one is most suitable when the control has specific constraints. In this work, we perform a comparative study on the efficacy of three reinforcement learning algorithms: tabular Q-learning, deep Q-learning, and policy gradient, as well as two non-machine-learning methods: stochastic gradient descent and Krotov algorithms, in the problem of preparing a desired quantum state. We found that overall, the deep Q-learning and policy gradient algorithms outperform others when the problem is discretized, e.g. allowing discrete values of control, and when the problem scales up. The reinforcement learning algorithms can also adaptively reduce the complexity of the control sequences, shortening the operation time and improving the fidelity. Our comparison provides insights into the suitability of reinforcement learning in quantum control problems.

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。

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