High-capacity associative memory in a quantum-optical spin glass
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914038772924416 |
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| author | Marsh, Brendan P. Schuller, David Atri Ji, Yunpeng Hunt, Henry S. Ganguli, Surya Gopalakrishnan, Sarang Keeling, Jonathan Lev, Benjamin L. |
| author_facet | Marsh, Brendan P. Schuller, David Atri Ji, Yunpeng Hunt, Henry S. Ganguli, Surya Gopalakrishnan, Sarang Keeling, Jonathan Lev, Benjamin L. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12202 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | High-capacity associative memory in a quantum-optical spin glass Marsh, Brendan P. Schuller, David Atri Ji, Yunpeng Hunt, Henry S. Ganguli, Surya Gopalakrishnan, Sarang Keeling, Jonathan Lev, Benjamin L. Quantum Physics Disordered Systems and Neural Networks Quantum Gases Statistical Mechanics Atomic Physics 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. |
| title | High-capacity associative memory in a quantum-optical spin glass |
| topic | Quantum Physics Disordered Systems and Neural Networks Quantum Gases Statistical Mechanics Atomic Physics |
| url | https://arxiv.org/abs/2509.12202 |