使用机器学习量化未知的多量子比特纠
Yukun Wang1, Shaoxuan Wang1, Jincheng Xing1
1Beijing Key Laboratory of Petroleum Data Mining, China University of Petroleum, Beijing 102249, China.
Entropy (Basel, Switzerland)
|February 26, 2025
概括
这项研究使用机器学习来精确量化多方纠,这是一个关键的量子技术资源. 这种新的方法避免了对未知的量子状态进行复杂的计算和广泛的测量.
科学领域:
- 量子信息科学 量子信息科学
- 机器学习应用 机器学习应用
- 量子计算是一种量子计算.
背景情况:
- 纠对于量子技术至关重要,但量化多方纠在计算上具有挑战性.
- 现有的方法往往需要完整的量子状态信息,并且具有很高的复杂性.
- 准确的纠量化对于推进量子计算和通信至关重要.
研究的目的:
- 开发一种基于机器学习的方法,用于精确量化未知的多方纠.
- 为了克服传统纠测量的计算复杂性和数据要求.
- 为了在大型量子系统中实现高效的纠表征.
主要方法:
- 训练神经网络使用平方纠 (SE) 和局部测量结果统计.
- 使用机器学习来建模测量数据和纠之间的非线性关系.
- 采用本地测量数据,避免需要全球测量或量子状态断层扫描.
主要成果:
- 实现了未知多方纠状态的高精度量化.
- 证明了所需测量的线性缩放,显著降低了计算负载.
- 展示了对噪声的强度和适用于纯量子状态和混合量子状态的适用性.
结论:
- 拟议的机器学习方法有效地量化了多方纠的高精度.
- 这种方法为传统的纠量化技术提供了可扩展和高效的替代方案.
- 这些发现为复杂量子系统中纠的实际表征铺平了道路.
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