# Solving combinational optimization problems with evolutionary single-pixel imaging

arXiv: https://arxiv.org/abs/2210.05923 · HTML: https://mlsanity.com/paper/2210.05923/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 5 of 5)

- [Optical machine learning with incoherent light and a single-pixel detector](https://mlsanity.com/paper/1904.10851/) (2019)
- [Single-Pixel Pattern Recognition with Coherent Nonlinear Optics](https://mlsanity.com/paper/2010.02273/) (2020)
- [Self-evolving ghost imaging](https://mlsanity.com/paper/2008.00648/) (2020)
- [Optical secret sharing with cascaded metasurface holography](https://mlsanity.com/paper/2110.03265/) (2021)
- [Image-free multi-character recognition](https://mlsanity.com/paper/2112.10587/) (2021)

## Abstract (arXiv)

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.