Noise-injected analog Ising machines enable ultrafast statistical sampling and machine learning

Ising machines are promising for neural network training and combinatorial optimization. However, their slow statistical sampling hinders neural network training efficiency. This paper introduces a universal concept for ultrafast statistical sampling using analog Ising machines by injecting noise. Experiments with an opto-electronic Ising machine demonstrate accurate Boltzmann distribution sampling and unsupervised neural network training, matching software-based methods in accuracy. Simulations show orders-of-magnitude faster sampling than software methods, enabling Ising machines for machine learning and other applications beyond combinatorial optimization.

Fabian Böhm, Diego Alonso-Urquijo, Guy Verschaffelt, Guy Van der Sande