Ultrafast Boltzmann Sampling using Photonic Ising Machines for Machine Learning

Ising machines have emerged as a promising computational concept that specializes in solving resource intensive optimization problems. Their operation is based on mapping a problem’s cost function to a network of coupled Ising spins whose energy is described by the Ising Hamiltonian. Such a spin system can be emulated on a network of bistable analog oscillators and the tendency of this system to evolve to the lowest energy configuration is then used to find optimal solutions of a problem orders of magnitude faster than digital computers. In optics, photonic Ising machines based on coupled lasers, degenerate optical parametric oscillators and polariton condensates have recently demonstrated potential performance gains over digital hardware. As an alternative to these complex systems, we have developed a photonic Ising machine based on opto-electronic oscillators [1], which can utilize telecom-grade components and potentially yields high bandwidths of 40 GHz. We have shown that this opto-electronic Ising machine can have similar and in specific cases even significantly better performance on solving combinatorial optimization tasks as compared to the state of the art.