Quantum Illumination Optimization

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum Illumination Optimization (QIO) is a novel quantum optimization algorithm designed to achieve near-optimal solutions in a limited time frame. Traditional optimization methods often require extensive computational resources and can be slow to converge. QIO leverages the inherent randomness and adaptive nature of quantum computation to dynamically adjust the optimization process, effectively guiding the algorithm towards the optimal solution without requiring exhaustive computation. This paper details the core mechanism of QIO, explores its potential advantages, and discusses its theoretical foundations. We present a framework for applying QIO to a specific optimization problem, demonstrating its effectiveness through simulations and preliminary analysis. The core claim is that QIO can significantly reduce the computational time required for finding optimal solutions compared to conventional algorithms.

DOI: 10.5281/zenodo.22252793

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