自适应人工智能用于脉冲级量子控制
Adaptive AI for Pulse-Level Quantum Control
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中文总结 AI 辅助
本文提出基于深度Q学习的自适应智能体，用于脉冲级量子控制，在模拟超导设备上优化氢分子基态制备，实现高保真度并优于随机搜索基线。
中文摘要 AI 辅助
控制变分量子本征求解器（ctrl-VQE）直接优化微波脉冲，以实现更快、更低误差的量子态制备，但其连续控制景观需要高效的搜索策略。我们证明，基于深度Q学习网络的强化学习智能体能够仅利用系统参数和奖励函数自主发现高性能脉冲序列。该方法对超导量子比特平台完全通用，无需假设态（ansatz），并以与硬件约束兼容的纳秒分辨率运行。作为概念验证，我们将该方法应用于在模拟超导设备上制备氢分子的基态。该智能体持续识别出优化的控制序列，实现高保真度并优于随机搜索基线。这些结果凸显了自适应学习作为一种有前景的、硬件就绪的脉冲级量子控制框架。
英文摘要
The Control Variational Quantum Eigensolver (ctrl-VQE) directly optimizes microwave pulses to enable faster and lower-error quantum-state preparation, but its continuous control landscape re- quires efficient search strategies. We demonstrate that a reinforcement-learning agent based on a deep Q learning network can autonomously discover high-performance pulse sequences using only system parameters and a reward function. The approach is fully general for superconducting qubit platforms, requires no ansatz, and operates at nanosecond resolution compatible with hardware con- straints. As a proof of concept, we apply the method to ground-state preparation of the Hydrogen molecule on a simulated superconducting device. The agent consistently identifies optimized control sequences that achieve high fidelity and outperform random-search baselines. These results highlight adaptive learning as a promising hardware-ready framework for pulse-level quantum control.
发表机构
- Southern Methodist University（南方卫理公会大学）
机构由 AI 辅助整理，请以论文原文为准。
