Adaptive Quantum Potential Optimization Algorithm

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces an Adaptive Quantum Potential Optimization Algorithm (AQPO) designed to enhance the efficiency and stability of quantum computations. Traditional quantum optimization methods often rely on fixed parameters, limiting the algorithm's adaptability and potential for improvement. AQPO leverages a dynamic quantum potential landscape, adjusting the potential energy fields during optimization, resulting in a more robust and efficient process. We present a novel framework incorporating a feedback loop that continuously monitors and adapts the potential, leading to significantly improved performance in various quantum algorithms. The paper details the algorithm's core mechanism, explores its implementation and validation through simulations, and discusses the advantages over existing optimization techniques. This research explores the potential of dynamically adjusting quantum potential to improve the performance of quantum computations.

DOI: 10.5281/zenodo.22445214

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