量子平衡传播用于基于Onsager互惠性的量子系统的高效训练
Clara C Wanjura1, Florian Marquardt2,3
1Max Planck Institute for the Science of Light, Erlangen, Germany. clara.wanjura@mpl.mpg.de.
Nature communications
|July 17, 2025
概括
研究人员开发了一种量子版本的平衡传播 (EP) 用于训练神经形态硬件. 这种新方法使用量子系统高效地提取训练梯度,使量子计算和人工智能的新应用成为可能.
科学领域:
- 量子计算是一种量子计算.
- 人工智能的人工智能
- 神经形态硬件的神经形态硬件
背景情况:
- 机器学习和人工智能推动了对节能硬件的需求.
- 神经形态系统提供了替代硬件,但缺乏有效的训练方法.
- 均衡传播 (EP) 是一种研究的训练方法,用于基于经典能源的模型.
研究的目的:
- 导出一个量子版本的平衡传播 (EP).
- 为量子神经形态系统提供高效的基于物理的培训.
- 通过线性响应实验提取任意量子系统的训练梯度.
主要方法:
- 建立了EP和Onsager互惠之间的联系.
- 导出了EP的量子版本,适用于任意的量子系统.
- 使用线性响应实验来提取训练梯度.
主要成果:
- 引入了量子EP,用于训练量子系统.
- 通过单个线性响应实验证明了梯度提取.
- 在量子相位发现,传感和相位边界探索中的插图应用.
结论:
- 量子EP为量子神经形态硬件提供了一种高效的训练方法.
- 这种方法可以解决经典难以解决的问题,如量子相位发现.
- 该方案与各种量子模拟平台兼容.
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