Parallel spatial photonic Ising machine using spatial multiplexing for accelerating combinatorial optimization

A spatial photonic Ising machine (SPIM) handles large-scale combinatorial optimization problems owing to optical processing with spatial parallelism. However, iterative feedback in the search for optimal solutions limits processing speed even though the Ising Hamiltonian is computed optically. We propose a parallel spatial photonic Ising machine (pSPIM) utilizing spatial multiplexing to search for an optimal solution efficiently. By employing grating patterns and encoding multiple sets of Ising spins in a phase distribution, several Ising Hamiltonians are computed simultaneously. We demonstrated that Max-Cut problems requiring 100 Ising spins are solved faster as the number of processing units increases. In addition, combining the multicomponent model with parallel processing allows for efficient searching for optimal solutions to problems represented by using interaction matrixes with a rank greater than one. The pSPIM achieves high-speed searching of optimal solutions of large-scale combinatorial optimization problems.