Dynamic Adaptive Quantum Potential Optimization Algorithm

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper introduces a dynamic adaptive quantum potential optimization algorithm designed to enhance quantum computation performance by automatically adjusting optimization parameters in response to changes in the quantum potential. The algorithm leverages a dynamically adjusted parameter set to efficiently navigate complex quantum states, mitigating the limitations of static optimization methods. We present a comprehensive framework demonstrating the algorithm's effectiveness across various quantum systems and benchmark scenarios. The core mechanism centers around a feedback loop that continuously monitors the quantum potential, iteratively refining the optimization parameters to achieve optimal results. The algorithm's adaptability and efficiency make it a promising advancement in quantum algorithm design.

DOI: 10.5281/zenodo.22321214

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