Solving combinational optimization problems with evolutionary single-pixel imaging

TL;DR

This paper introduces an optical single-pixel imaging scheme utilizing an Ising machine model to solve combinational optimization problems like number partition and graph maximum cut, demonstrating both simulated and experimental success.

Contribution

It presents a novel SPI-based approach implementing an Ising machine model for solving combinational optimization problems, expanding SPI applications beyond image processing.

Findings

Successfully optimized Hamiltonian functions with evolutionary illumination patterns.

Demonstrated effectiveness through simulated results.

Validated approach with experimental results.

Abstract

Single-pixel imaging (SPI) is a novel optical imaging technique by replacing the pixelated sensor array in a conventional camera with a single-pixel detector. In previous works, SPI is usually used for capturing object images or performing image processing tasks. In this work, we propose a SPI scheme for processing other types of data in addition to images. An Ising machine model is implemented optically with SPI for solving combinational optimization problems including number partition and graph maximum cut. Simulated and experimental results show that our proposed scheme can optimize the Hamiltonian function with evolutionary illumination patterns.