基于神经网络的近似戈特斯曼-基塔耶夫-普雷斯基尔代码的设计
Yexiong Zeng1,2, Wei Qin1,3,4, Ye-Hong Chen1,2,5,6
1RIKEN, Theoretical Quantum Physics Laboratory, Cluster for Pioneering Research, Wakoshi, Saitama 351-0198, Japan.
Physical review letters
|February 28, 2025
概括
神经网络优化了Gottsman-Kitaev-Preskill (GKP) 编码用于量子计算. 优化的GKP代码使用更少的挤压状态,改善错误纠正和减少复杂性.
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.
- 量子错误纠正方法 量子错误纠正方法
背景情况:
- 戈特斯曼-基塔耶夫-普雷斯基尔 (GKP) 编码对于连续变量的容错量子计算至关重要.
- 理想的GKP编码是非物理的;近似版本是实用但复杂的.
- 传统的近似GKP状态需要多个缩的连贯状态,复杂的准备.
研究的目的:
- 为了最大限度地减少代码单词复杂性和错误纠正能力之间的权衡,在近似的GKP状态下.
- 开发一种方法,使用神经网络生成最佳的近似GKP状态.
- 为了提高GKP代码的性能,用于量子错误校正.
主要方法:
- 利用神经网络生成最佳的近似GKP状态.
- 将优化的GKP代码与传统代码的性能进行比较.
- 分析了所需的压缩连贯状态的数量和稳定器操作员的复杂性.
主要成果:
- 优化 GKP 代码的性能优于传统代码.
- 实现了有效的错误校正,显著减少了挤压一致状态.
- 在9.55dB的压缩下,压缩连贯状态的数量减少到三分之一.
结论:
- 神经网络优化大大降低了GKP代码单词的复杂性.
- 与传统方法相比,优化的 GKP 代码提供了更好的错误纠正能力.
- 这种方法为更实用的容错量子计算铺平了道路.
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