Adaptive Optimization Algorithm Based on Quantum Error Correction

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces an adaptive optimization algorithm based on quantum error correction, designed to dynamically adjust optimization strategies in response to noise and errors during computation. Traditional optimization methods often rely on fixed parameters, limiting their adaptability to complex environments. Leveraging quantum mechanics, specifically the entanglement and superposition of quantum states, we propose a novel approach that allows the algorithm to continuously refine its optimization path and achieve improved precision. The core mechanism involves quantum state manipulation, error correction, and dynamic adjustment of optimization parameters based on the observed noise and errors. We demonstrate the effectiveness of this algorithm through simulations, illustrating its ability to enhance solution accuracy and robustness compared to conventional optimization techniques. The paper also discusses the potential advantages and challenges associated with this approach, highlighting the significance of quantum error correction in optimizing complex computational systems.

DOI: 10.5281/zenodo.22263111

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