Quantum Algorithm Self-Adaptive Optimization

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

Quantum algorithm self-adaptive optimization aims to enhance the efficiency of quantum algorithms by dynamically adjusting parameters during the optimization process. This approach leverages the inherent self-correlation properties of quantum algorithms to automatically optimize parameters, potentially leading to improved performance compared to traditional optimization techniques. This paper presents a novel method for implementing this self-adaptive optimization, detailing the core mechanism and exploring its potential benefits. The research focuses on a specific quantum algorithm, the variational quantum eigensolver (VQE), to demonstrate the efficacy of this approach. The study examines the impact of parameter adjustments on the solution quality and computational cost, ultimately highlighting the advantages of automated parameter tuning.

DOI: 10.5281/zenodo.22154467

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