Abstract

Solving combinational optimization problems, which are known to be non-deterministic polynomial hard, is useful in a wide range of fields, including transportation and finance. A spatial photonic Ising machine (SPIM) enables problems with a large number of combinations to be solved by encoding the spin variables into phase [1]. However, it is difficult to handle various types of problems because the degree of freedom of the interaction between spin variables is equal to the number of spin variables. In this study, we propose a method for expanding the flexibility of the interaction between spin variables by parallel processing using space-division multiplexing. Space-division multiplexing can be used to process components of the Ising Hamiltonian simultaneously, and a wide range of Ising Hamiltonians can be represented by synthesizing the individual results.

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