近日，美国斯坦福大学Benjamin L. Lev团队报道了量子光学自旋玻璃中的高容量联想记忆。相关论文于2026年9月3日发表在《科学》杂志上。

Hopfield神经网络利用全连接自旋存储记忆，并通过平衡动力学回忆这些记忆。存储过多记忆会阻碍回忆，因为阻挫会导致网络中出现指数级数量的虚假模式，使网络变成自旋玻璃。尽管如此，在量子光学非平衡动力学条件下，记忆回忆可以得到恢复甚至增强，因为此时虚假模式可作为可靠的记忆。

研究组在由原子和光子组成的驱动耗散自旋玻璃中，实验观察到具有高存储容量的联想记忆。在16自旋网络中，该容量比赫布学习下的Hopfield模型高出多达七倍。原子运动通过动态修改连接性（类似于神经网络中的短期突触可塑性）来提升容量，从而实现了量子光学系统中学习机制的前驱体。

附：英文原文

Title: High-capacity associative memory in a quantum-optical spin glass

Author: Brendan P. Marsh, David Atri Schuller, Yunpeng Ji, Henry S. Hunt, Surya Ganguli, Sarang Gopalakrishnan, Jonathan Keeling, Benjamin L. Lev

Issue&Volume: 2026-09-03

Abstract: The Hopfield neural network stores memories using all-to-all-coupled spins and recalls those memories through equilibrium dynamics. Storing too many hampers recall because frustration causes an exponential number of spurious patterns to arise as the network becomes a spin glass. Despite this, memory recall can be restored, and even enhanced, under quantum-optical nonequilibrium dynamics because spurious patterns can now serve as reliable memories. We experimentally observe associative memory with high storage capacity in a driven-dissipative spin glass made of atoms and photons. The capacity surpasses that of the Hopfield model under Hebbian learning by up to seven-fold in a sixteen-spin network. Atomic motion boosts capacity by dynamically modifying connectivity akin to short-term synaptic plasticity in neural networks, realizing a precursor to learning in a quantum-optical system.

DOI: aec3917

Source: https://www.science.org/doi/10.1126/science.aec3917