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

• URL: http://arxiv.org/abs/2509.12202v1
• Date: Mon, 15 Sep 2025 17:59:30 GMT
• Title: High-capacity associative memory in a quantum-optical spin glass
• Authors: Brendan P. Marsh, David Atri Schuller, Yunpeng Ji, Henry S. Hunt, Surya Ganguli, Sarang Gopalakrishnan, Jonathan Keeling, Benjamin L. Lev,
• Abstract summary: We experimentally observe associative memory with high storage capacity in a driven-dissipative spin glass made of atoms and photons.<n>Capacity surpasses the Hopfield limit by up to seven-fold in a sixteen-spin network.
• Score: 8.804774832130603
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Latent Structured Hopfield Network for Semantic Association and Retrieval [52.634915010996835]
  Episodic memory enables humans to recall past experiences by associating semantic elements such as objects, locations, and time into coherent event representations.<n>We propose the Latent Structured Hopfield Network (LSHN), a framework that integrates continuous Hopfield attractor dynamics into an autoencoder architecture.<n>Unlike traditional Hopfield networks, our model is trained end-to-end with gradient descent, achieving scalable and robust memory retrieval.
  arXiv  Detail & Related papers  (2025-06-02T04:24:36Z)
• Time Symmetries of Quantum Memory Improve Thermodynamic Efficiency [49.1574468325115]
  Quantum memory offers a continuum of possible time-reversal symmetries.<n>This enables the design of quantum memories that minimize irreversibility.<n>As a result, quantum memory reduces energy dissipation several orders of magnitude below classical memory.
  arXiv  Detail & Related papers  (2025-01-08T22:30:35Z)
• Storing overlapping associative memories on latent manifolds in low-rank spiking networks [5.041384008847852]
  We revisit the associative memory problem in light of advances in understanding spike-based computation.<n>We show that the spiking activity for a large class of all-inhibitory networks is situated on a low-dimensional, convex, and piecewise-linear manifold.<n>We propose several learning rules, and demonstrate a linear scaling of the storage capacity with the number of neurons, as well as robust pattern completion abilities.
  arXiv  Detail & Related papers  (2024-11-26T14:48:25Z)
• Dense Associative Memory Through the Lens of Random Features [48.17520168244209]
  Dense Associative Memories are high storage capacity variants of the Hopfield networks. We show that this novel network closely approximates the energy function and dynamics of conventional Dense Associative Memories.
  arXiv  Detail & Related papers  (2024-10-31T17:10:57Z)
• Emulating Complex Synapses Using Interlinked Proton Conductors [17.304569471460013]
  We experimentally realize the Benna-Fusi artificial complex synapse. The memory consolidation from coupled storage components is revealed by both numerical simulations and experimental observations. Our experimental realization of the complex synapse suggests a promising approach to enhance memory capacity and to enable continual learning.
  arXiv  Detail & Related papers  (2024-01-26T18:16:06Z)
• Optimal storage capacity of quantum Hopfield neural networks [0.0]
  It is a challenging open problem to analyze quantum associative memories with an extensive number of patterns. We propose and explore a general method for evaluating the maximal storage capacity of quantum neural network models.
  arXiv  Detail & Related papers  (2022-10-14T15:21:21Z)
• Quantum associative memory with a single driven-dissipative nonlinear oscillator [0.0]
  We propose a realization of associative memory with a single driven-dissipative quantum oscillator. The model can improve the storage capacity of discrete neuron-based systems in a large regime. We show that the associative-memory capacity is inherently related to the existence of a spectral gap in the Liouvillian superoperator.
  arXiv  Detail & Related papers  (2022-05-19T12:00:35Z)
• Cross-Frequency Coupling Increases Memory Capacity in Oscillatory Neural Networks [69.42260428921436]
  Cross-frequency coupling (CFC) is associated with information integration across populations of neurons. We construct a model of CFC which predicts a computational role for observed $theta - gamma$ oscillatory circuits in the hippocampus and cortex. We show that the presence of CFC increases the memory capacity of a population of neurons connected by plastic synapses.
  arXiv  Detail & Related papers  (2022-04-05T17:13:36Z)
• Parametrized constant-depth quantum neuron [56.51261027148046]
  We propose a framework that builds quantum neurons based on kernel machines. We present here a neuron that applies a tensor-product feature mapping to an exponentially larger space. It turns out that parametrization allows the proposed neuron to optimally fit underlying patterns that the existing neuron cannot fit.
  arXiv  Detail & Related papers  (2022-02-25T04:57:41Z)
• Astrocytes mediate analogous memory in a multi-layer neuron-astrocytic network [52.77024349608834]
  We show how a piece of information can be maintained as a robust activity pattern for several seconds then completely disappear if no other stimuli come. This kind of short-term memory can keep operative information for seconds, then completely forget it to avoid overlapping with forthcoming patterns. We show how arbitrary patterns can be loaded, then stored for a certain interval of time, and retrieved if the appropriate clue pattern is applied to the input.
  arXiv  Detail & Related papers  (2021-08-31T16:13:15Z)
• Enhancing associative memory recall and storage capacity using confocal cavity QED [15.696215759892052]
  We introduce a near-term experimental platform for realizing an associative memory. It can simultaneously store many memories by using spinful bosons coupled to a multimode optical cavity. We show that this nonequilibrium quantum-optical scheme has significant advantages for associative memory over Glauber dynamics.
  arXiv  Detail & Related papers  (2020-09-02T17:59:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.