在变量量子算法中,测量噪声对逃逸的影响
Eriko Kaminishi1, Takashi Mori2, Michihiko Sugawara3
1Quantum Computing Center, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa, 223-8522, Japan. kaminishi@keio.jp.
Scientific reports
|February 17, 2026
概括
在变量量子Eigensolver (VQE) 优化中的测量噪声,使用随机梯度下降 (SGD),有助于逃避点. 通过学习速率和测量计数逃脱时间尺度,提供对量子算法优化的洞察力.
科学领域:
- 量子计算是一种量子计算.
- 机器学习优化优化
背景情况:
- 随机梯度下降 (SGD) 对于经典机器学习和变量量子自身解决 (VQE) 是至关重要的.
- 量子硬件实现的VQE容易受到不可避免的测量射击噪声的影响.
研究的目的:
- 分析测量噪声对VQE优化动态的影响.
- 调查噪音如何帮助在非凸损失景观中逃离点.
主要方法:
- 在测量噪声下对VQE优化动态的模拟.
- 使用SGD和随机微分方程 (SDEs) 进行理论分析.
主要成果:
- 从点逃跑的时间表现出与学习率 (η) 和测量次数的权力规律缩放.
- 一个连续时间的SDE近似有效地模拟了短暂的逃逸动态.
- 学习率和反向测量计数的乘积表示有效的噪声强度.
结论:
- 在VQE优化中,测量噪声可以有利于逃避位点.
- SDEs提供了对过渡动态的准确见解,澄清了它们的适用性,尽管存在静止状态的担忧.
- 这项研究提高了对噪声在VQE优化过程中的作用的理解.
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