通过神经网络进行基于相关性模式的连续变量纠检测
Xiaoting Gao1,2, Mathieu Isoard2, Fengxiao Sun1,3
1State Key Laboratory for Mesoscopic Physics, School of Physics, Frontiers Science Center for Nano-optoelectronics, & Collaborative Innovation Center of Quantum Matter, Peking University, Beijing 100871, China.
Physical review letters
|June 15, 2024
概括
我们开发了一个神经网络,使用相关性模式来检测复杂状态中的量子纠. 这种方法比传统技术更准确,即使数据有限.
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.
- 机器学习应用 机器学习应用
背景情况:
- 连续变量 (CV) 非高斯态在量子信息任务中提供了显著的优势.
- 鉴定这些状态是具有挑战性的,因为指数级的信息增长.
研究的目的:
- 开发一个神经网络,以有效地检测心血管纠.
- 为了使纠检测在没有全态断层扫描的情况下.
主要方法:
- 利用一个神经网络训练的相关性模式从homodyne检测.
- 采用了明星层次结构来排名培训状态.
- 应用尺寸缩小算法用于可视化.
主要成果:
- 神经网络准确地检测了高斯和非高斯状态中的纠.
- 获得了比有限数据的最大概率断层扫描更高的准确性.
- 视觉化揭示了纠和非纠状态之间的清晰界限.
结论:
- 神经网络提供了一种有效的方法,用于实验检测CV量子相关性.
- 证明了神经网络在量子信息处理中的潜力.
- 便于对不同纠证人的比较和理解.
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