Solving combinational optimization problems with evolutionary single-pixel imaging
This work addresses combinatorial optimization problems for researchers in optical computing and imaging, representing an incremental extension of single-pixel imaging to non-image data.
The authors tackled the problem of solving combinatorial optimization problems by proposing a single-pixel imaging scheme that implements an Ising machine model optically, achieving optimization of the Hamiltonian function with evolutionary illumination patterns in simulated and experimental results.
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.