通过深度强化学习产生极性分子的纠生成
Zuo-Yuan Zhang1, Zhaoxi Sun2, Tao Duan3
1School of Physical Science and Technology, Yangzhou University, Yangzhou 225009, China.
Journal of chemical theory and computation
|February 6, 2024
概括
深度强化学习 (RL) 在极性分子中产生分子间纠,用于量子计算. 这种机器学习方法发现了可行的控制场,用于高保真度的两位和三位量子位纠状态.
科学领域:
- 量子信息处理是一种量子信息处理.
- 分子量子计算是一种分子量子计算.
- 量子控制是一种量子控制.
背景情况:
- 极极分子提供了可扩展的量子信息处理,因为它们具有远程二极管-二极管相互作用.
- 超冷极分子,特别是CaF,被用作外部电场中的量子位.
- 创建分子间纠对于推进量子计算平台至关重要.
研究的目的:
- 研究深度强化学习 (RL) 的应用,用于在极性分子系统中产生分子间纠.
- 发现最佳的依赖时间的控制场,用于创建多量子位纠状态.
- 与现有方法相比,分析纠生成的可行性和可靠性.
主要方法:
- 使用电场梯度中的合超冷CaF分子作为量子位.
- 使用深度强化学习代理来发现时间依赖的控制场.
- 分析纠忠实性和消极性,以及分子群体动态.
主要成果:
- RL代理人成功地发现了控制场,以引导分子系统进入两个和三个量子位纠状态,具有高保真度.
- 在训练中分析了纠忠实性和消极性.
- 介绍了发现的控制场下的人口动态.
结论:
- 深度强化学习是解决极性分子系统中量子控制问题的强大工具.
- 发现的控制场参数在实验上可能比最佳控制理论中的参数更可行.
- 这项工作突出了机器学习在推进分子量子信息处理方面的潜力.
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