通过多副本神经网络方法进行资源效率高的量子相关性测量
Patrycja Tulewicz1,2, Karol Bartkiewicz3,4, Adam Miranowicz3,4
1Institute of Spintronics and Quantum Information, Faculty of Physics and Astronomy, Adam Mickiewicz University, Poznań, 61-614, Poland. pattul@amu.edu.pl.
Scientific reports
|November 19, 2025
概括
我们开发了一种使用人工神经网络 (ANN) 和多重复制测量的新方法,以高效地描述量子纠和非局部性. 这种人工智能辅助的方法与传统的量子状态断层扫描 (QST) 相比,大大减少了测量需求.
科学领域:
- 量子信息科学 量子信息科学
- 量子计算是一种量子计算.
- 人工智能的人工智能
背景情况:
- 描述复杂的量子性质,如纠和非局部性,至关重要但具有挑战性.
- 传统的方法,如量子状态断层扫描 (QST) 需要随着系统大小的指数级扩展资源.
- 对于当前杂的中等尺度量子 (NISQ) 硬件,需要有效和强大的测量策略.
研究的目的:
- 提出和实验验证一种资源效率高的方法来测量量子相关性.
- 为了减少对量化纠和贝尔非局部性的信息要求.
- 为了提高NISQ设备上的量子测量的噪声稳定性.
主要方法:
- 将多副本测量与人工神经网络 (ANN) 结合起来,用于量子状态的表征.
- 使用沙普利 (SHAP) 分析来优化测量投影集.
- 在 IBMQ 量子处理器上使用跨子量子比特实现和测试该方法.
- 将结果与标准QST进行比较,并应用降噪技术.
主要成果:
- 与QST相比,实现了67%的测量需求减少.
- 在噪声通道下成功测量了维纳和霍罗德基状态的量子相关性.
- 训练有素的ANN可以从缩小的测量集准确估计纠和非局部度量.
- 证明了人工智能辅助多副本测量策略的提高噪声稳定性.
结论:
- 拟议的人工智能辅助多副本测量方法为量子相关性表征提供了QST的实用和资源高效的替代方案.
- 这种方法简化了复杂的量子测量,并提高了NISQ硬件上的错误稳定性.
- 它代表了实际量子信息处理和人工智能辅助量子计量学的重大进步.
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