Multiphoton quantum simulation of the generalized Hopfield memory model
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917353035399168 |
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| author | Zanfardino, Gennaro Paesani, Stefano Leuzzi, Luca Santagati, Raffaele Sciarrino, Fabio Illuminati, Fabrizio Ruocco, Giancarlo Leonetti, Marco |
| author_facet | Zanfardino, Gennaro Paesani, Stefano Leuzzi, Luca Santagati, Raffaele Sciarrino, Fabio Illuminati, Fabrizio Ruocco, Giancarlo Leonetti, Marco |
| contents | In the present work, we introduce, develop, and investigate a connection between multiphoton quantum interference, a core element of emerging photonic quantum technologies, and Hopfieldlike Hamiltonians of classical neural networks, the paradigmatic models for associative memory and machine learning in systems of artificial intelligence. Specifically, we show that combining a system composed of Nph indistinguishable photons in superposition over M field modes, a controlled array of M binary phase-shifters, and a linear-optical interferometer, yields output photon statistics described by means of a p-body Hopfield Hamiltonian of M Ising-like neurons +-1, with p = 2Nph. We investigate in detail the generalized 4-body Hopfield model obtained through this procedure and show that it realizes a transition from a memory retrieval to a memory black-out regime, i.e. a spin-glass phase, as the amount of stored memory increases. The mapping enables novel routes to the realization and investigation of disordered and complex classical systems via efficient photonic quantum simulators, as well as the description of aspects of structured photonic systems in terms of classical spin Hamiltonians. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_00111 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multiphoton quantum simulation of the generalized Hopfield memory model Zanfardino, Gennaro Paesani, Stefano Leuzzi, Luca Santagati, Raffaele Sciarrino, Fabio Illuminati, Fabrizio Ruocco, Giancarlo Leonetti, Marco Quantum Physics Disordered Systems and Neural Networks In the present work, we introduce, develop, and investigate a connection between multiphoton quantum interference, a core element of emerging photonic quantum technologies, and Hopfieldlike Hamiltonians of classical neural networks, the paradigmatic models for associative memory and machine learning in systems of artificial intelligence. Specifically, we show that combining a system composed of Nph indistinguishable photons in superposition over M field modes, a controlled array of M binary phase-shifters, and a linear-optical interferometer, yields output photon statistics described by means of a p-body Hopfield Hamiltonian of M Ising-like neurons +-1, with p = 2Nph. We investigate in detail the generalized 4-body Hopfield model obtained through this procedure and show that it realizes a transition from a memory retrieval to a memory black-out regime, i.e. a spin-glass phase, as the amount of stored memory increases. The mapping enables novel routes to the realization and investigation of disordered and complex classical systems via efficient photonic quantum simulators, as well as the description of aspects of structured photonic systems in terms of classical spin Hamiltonians. |
| title | Multiphoton quantum simulation of the generalized Hopfield memory model |
| topic | Quantum Physics Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2504.00111 |