Optical Spin Glasses

https://opg.optica.org/aop/abstract.cfm?URI=aop-18-2-421

Spin-glass theory emerged in the 1980s as a merger between theoretical physics and condensed matter. Soon, physicists realized that spin glasses serve as a paradigm for complex systems, as underscored by the 2021 Nobel Prize in Physics, and for applications in machine learning and neuroscience, with a profound connection with the Hopfield model and Boltzmann machines, subjects of the 2024 Nobel Prize in Physics. However, the connection with optics and photonics is even more profound and fundamental; this connection was identified as early as 1982, with the first realizations of optical neural networks. Thirty years later, the first experimental demonstration of a pillar of spin-glass theory, the replica symmetry breaking, was reported in photonics. Nowadays, many scientists consider photonics as an effective solution for new hardware in artificial intelligence, capable of reducing energy consumption in training large machine-learning modules, and also more suitable for realizing fully connected models that underpin modern data-driven analysis. The substantial equivalence between linear optical propagation and a system of interacting binary spins is now well recognized, triggering the development of a new family of devices for both classical and quantum computing. This review is intended to detail the work of the past twenty years concerning the link between spin-glass theory and optics. After a simple introduction to the main ideas of spin glasses, we start from the first works aimed at finding a direct experimental proof of ideas such as the landscape and ultrametricity; then we report on “linear optical spin glasses,” which refer to the photonic simulation of various Ising models for combinatorial optimization and interlinked with quantum computers; finally, we discuss the emerging field of “nonlinear optical spin glasses,” driven by the impressive progress in the realization of coherent Ising machines with parametric oscillators, that opened an new research direction driven by the cross-fertilization of advanced theoretical physics, artificial intelligence, classical and quantum nonlinear optics.

Emergent Equilibrium in All-Optical Single Quantum-Trajectory Ising Machines

We investigate the dynamics of multi-mode optical systems driven by two-photon processes and subject to non-local losses, incorporating quantum noise at the Gaussian level. Our findings show that the statistics from a single Gaussian quantum trajectory exhibit emergent thermal equilibrium governed by an Ising Hamiltonian encoded in the dissipative coupling between modes. The driving strength sets the system’s effective temperature relative to the oscillation threshold. Given the ultra-short time scales typical of all-optical devices, our study demonstrates that such multi-mode optical systems can operate as ultra-fast Boltzmann samplers, paving the way toward the realization of efficient hardware for combinatorial optimization, with promising applications in machine learning and beyond.

https://arxiv.org/abs/2412.12768

https://mathstodon.xyz/@nonlinearxwaves/113672283856089363

Deep Learning Enabled Transmission of Full-Stokes Polarization Images Through Complex Media

Polarization images offer crucial functionalities across multiple scientific domains, providing access to physical information beyond conventional measures such as intensity, phase, and spectrum of light. However, the challenge of transmitting polarization images through complex media has restricted their application in optical communication and imaging. Here, a novel approach utilizing deep learning for the transmission of full-Stokes polarization images through scattering media is presented. It is demonstrated that any input polarization image can be reconstructed in a single shot by employing only an intensity sensor. By supervised training of a deep neural network, high-accuracy full-Stokes reconstruction is achieved from the speckle pattern detected by an intensity camera. Leveraging the deep learning based polarization decoder, a polarization-colored encoding scheme is devised to enable increased-capacity data transmission through disordered channels. Fast, wavelength-independent, on-chip, polarization imaging in complex media enables the utilization of polarization-structured light in multimode fibres and opaque materials, unlocking new possibilities in optical communication, cryptography, and quantum technology.

https://doi.org/10.1002/lpor.202400626

EIC Project HEISINGBERG launched !

The EU project HEISINGBERG has started!

This project is funded by the EIC-Pathfinder initiative of the European Innovation Council for innovative Quantum technologies.

The project leverages our Spatial Ising Machine (SPIM) device and aims at a new generation of programmable and quantum annealers.

For details, have a look at the HEISINGBERG website.

HEISINGBERG logo and website

See also