Observation of associative-memory retrieval and spin-glass phases on a photonic quantum simulator
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917530108428288 |
|---|---|
| author | Giordani, Taira Zanfardino, Gennaro Leuzzi, Luca Bonfissuto, Enrico Caruccio, Eugenio Gasbarri, Gabriele Bossi, Mattia Rajan, Abhiram Albiero, Riccardo Ceccarelli, Francesco Spagnolo, Nicolò Santagati, Raffaele Paesani, Stefano Leonetti, Marco Osellame, Roberto Parisi, Giorgio Ruocco, Giancarlo Illuminati, Fabrizio Sciarrino, Fabio |
| author_facet | Giordani, Taira Zanfardino, Gennaro Leuzzi, Luca Bonfissuto, Enrico Caruccio, Eugenio Gasbarri, Gabriele Bossi, Mattia Rajan, Abhiram Albiero, Riccardo Ceccarelli, Francesco Spagnolo, Nicolò Santagati, Raffaele Paesani, Stefano Leonetti, Marco Osellame, Roberto Parisi, Giorgio Ruocco, Giancarlo Illuminati, Fabrizio Sciarrino, Fabio |
| contents | Models of interacting complex systems provide the fundamental statistical physics reference frame for the study and the understanding of associative memories, machine learning, and the dynamics of neural networks. On the other hand, simulating complex multi-synaptic interactions on a classical hardware is computationally demanding due to the super-linear scaling of the system complexity. Photonic quantum technologies provide a promising solution to these limitations by leveraging on their inherent speed and parallel processing ability in order to simulate complex networks. Recently, a connection between multiphoton processes and generalized $p$-body Hopfield models has been theoretically established. Here, we design and demonstrate an experimental platform that exploits single photons distributed across a set of optical modes, in which controlled arrays of binary phase shifters act as Ising-like neurons. We focus specifically on a fully connected Hopfield Hamiltonian with four-body local interaction terms, realized via two-photon processes. Through quantum simulations on programmable photonic processors, the study identifies three distinct regimes: a memory retrieval phase, a spin-glass memory "black-out" phase, and a paramagnetic phase. Experimental results confirm successful memory retrieval at low storage capacities and temperatures, where the system consistently relaxes to fixed points with high memory overlap, effectively reconstructing the stored patterns. Future research will extend the platform design to investigate networks with local or dilute interactions, while advances in the realization of scalable photonic circuits will enable architectures that encompass very large numbers of interacting spins. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_22922 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Observation of associative-memory retrieval and spin-glass phases on a photonic quantum simulator Giordani, Taira Zanfardino, Gennaro Leuzzi, Luca Bonfissuto, Enrico Caruccio, Eugenio Gasbarri, Gabriele Bossi, Mattia Rajan, Abhiram Albiero, Riccardo Ceccarelli, Francesco Spagnolo, Nicolò Santagati, Raffaele Paesani, Stefano Leonetti, Marco Osellame, Roberto Parisi, Giorgio Ruocco, Giancarlo Illuminati, Fabrizio Sciarrino, Fabio Quantum Physics Disordered Systems and Neural Networks Statistical Mechanics Models of interacting complex systems provide the fundamental statistical physics reference frame for the study and the understanding of associative memories, machine learning, and the dynamics of neural networks. On the other hand, simulating complex multi-synaptic interactions on a classical hardware is computationally demanding due to the super-linear scaling of the system complexity. Photonic quantum technologies provide a promising solution to these limitations by leveraging on their inherent speed and parallel processing ability in order to simulate complex networks. Recently, a connection between multiphoton processes and generalized $p$-body Hopfield models has been theoretically established. Here, we design and demonstrate an experimental platform that exploits single photons distributed across a set of optical modes, in which controlled arrays of binary phase shifters act as Ising-like neurons. We focus specifically on a fully connected Hopfield Hamiltonian with four-body local interaction terms, realized via two-photon processes. Through quantum simulations on programmable photonic processors, the study identifies three distinct regimes: a memory retrieval phase, a spin-glass memory "black-out" phase, and a paramagnetic phase. Experimental results confirm successful memory retrieval at low storage capacities and temperatures, where the system consistently relaxes to fixed points with high memory overlap, effectively reconstructing the stored patterns. Future research will extend the platform design to investigate networks with local or dilute interactions, while advances in the realization of scalable photonic circuits will enable architectures that encompass very large numbers of interacting spins. |
| title | Observation of associative-memory retrieval and spin-glass phases on a photonic quantum simulator |
| topic | Quantum Physics Disordered Systems and Neural Networks Statistical Mechanics |
| url | https://arxiv.org/abs/2605.22922 |