Quantum-State-Based Adaptive Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel quantum-state-based adaptive optimization algorithm designed to achieve precise optimization of algorithms. Traditional optimization methods rely on explicit algorithm design, while quantum mechanics offers capabilities that may provide a significant advantage in certain scenarios. This work explores the potential of utilizing quantum states to dynamically adjust parameters within an optimization process, aiming for improved accuracy and efficiency compared to conventional approaches. We detail the core mechanism of this algorithm, highlighting its unique application of quantum state manipulation to guide the optimization process. This research contributes to the field of adaptive optimization by leveraging quantum phenomena to enhance algorithmic performance.

DOI: 10.5281/zenodo.22184487

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