2025/09/15 by Brendan P. Marsh, Brendan Marsh, Marsh, Brendan P. +15 · 1 voice
Computer Science · Engineering · #Advanced Memory and Neural Computing #Artificial neural network #Associative property #Bidirectional associative memory #Content-addressable memory #Hopfield network #Neural Networks and Applications #Neural Networks and Reservoir Computing #Recall #Spin glass #Spurious relationship
paper · pdf · doi:10.48550/arxiv.2509.12202
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/09/15 · openalex created_date 2025/10/12 · openalex updated_date 2026/08/05
The Hopfield model describes a neural network that stores memories using all-to-all-coupled spins. Memory patterns are recalled under equilibrium dynamics. Storing too many patterns breaks the associative recall process because frustration causes an exponential number of spurious patterns to arise as the network becomes a spin glass. Despite this, memory recall in a spin glass 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 the Hopfield limit 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.