Quantum Adaptive Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum Adaptive Optimization is a novel quantum optimization algorithm designed to address the challenges posed by large-scale optimization problems. Traditional optimization algorithms often rely on classical computation, limiting their effectiveness to relatively small problem sizes. This paper introduces a quantum-enhanced optimization approach that leverages quantum properties such as superposition and entanglement to achieve significantly improved performance. The core mechanism centers around utilizing quantum phenomena to guide the search process, resulting in efficient optimization solutions. This work presents a framework for tackling complex problems with enhanced computational capabilities and offers a potential shift towards more powerful optimization techniques.

DOI: 10.5281/zenodo.22195437

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