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

Suguru Shimomura, Jun Tanida, Yusuke Ogura

|November 4, 2025

Summary

This summary is machine-generated.

A new parallel spatial photonic Ising machine (pSPIM) speeds up solving complex optimization problems. This optical computing approach uses spatial multiplexing for faster, more frequent optimal solutions in large-scale combinatorial problems.

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Area of Science:

Physics
Computer Science
Optical Engineering

Background:

Spatial photonic Ising machines (SPIMs) leverage optical processing and spatial parallelism for combinatorial optimization.
Iterative feedback in SPIMs limits processing speed despite optical computation of the Ising Hamiltonian.

Purpose of the Study:

To propose and demonstrate a parallel spatial photonic Ising machine (pSPIM) for efficient combinatorial optimization.
To enhance the speed and efficiency of solving large-scale optimization problems using optical computing.

Main Methods:

Utilizing spatial multiplexing with grating patterns to encode multiple Ising spin configurations.
Computing multiple Ising Hamiltonians simultaneously through phase distribution.
Demonstrating pSPIM on max-cut problems with 100 Ising spins.

Main Results:

Optimal solutions for max-cut problems were obtained more frequently with increased processing units.
Parallel processing combined with a multicomponent model efficiently solves problems with rank-greater-than-one interaction matrices.
The pSPIM spin-update strategy proves effective for large-scale combinatorial optimization.

Conclusions:

The pSPIM offers a significant advancement in solving large-scale combinatorial optimization problems.
Spatial multiplexing and parallel processing are key to overcoming the speed limitations of traditional SPIMs.
pSPIM provides an efficient optical computing approach for complex optimization tasks.