通过量子强化学习学习,在没有临界减速的情况下朝着海森堡极限迈进
Hang Xu1, Tailong Xiao1,2,3, Jingzheng Huang1,2,3
1Shanghai Jiao Tong University, State Key Laboratory of Advanced Optical Communication Systems and Networks, Institute for Quantum Sensing and Information Processing, Shanghai 200240, People's Republic of China.
Physical review letters
|April 11, 2025
概括
量子强化学习 (QRL) 通过发现最佳门序列,可以实现更快,更准确的量子传感. 这种方法即使在噪音条件下也能达到高精度,进步了量子增强传感技术.
科学领域:
- 量子物理学的量子物理学
- 量子信息科学是一种量子信息科学.
- 先进的传感技术是先进的传感技术.
背景情况:
- 临界基态对于量子增强传感至关重要.
- 亚亚巴特进化,一种传统的方法,可以限制量子传感的优势.
- 量子多体系统中的奇特相位图带来了独特的挑战.
研究的目的:
- 提出一个量子强化学习 (QRL) 增强的关键传感协议.
- 探索未知磁场的量子多体系统中的感知精度.
- 克服传统国家制备方法的局限性.
主要方法:
- 使用量子强化学习 (QRL) 来发现最佳的门序列.
- 从制备的产品状态开始.
- 调查局部和全球政权与外部磁场的传感精度.
主要成果:
- 通过QRL学习的序列达到有限的量子速度限制.
- 该协议在任意大小的系统中展示了有效的泛化.
- 海森伯格和超海森伯格极限被稳定地实现,即使使用噪声和保利测量.
结论:
- 对于精确的量子状态准备,QRL是非常有效的.
- 拟议的协议推进了可扩展,高精度的量子关键传感.
- 这种方法提供了一种强大的方法,用于在具有挑战性的环境中进行量子增强的传感.
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