Quantum Potential Dynamics and Optimization

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper introduces a novel optimization algorithm based on dynamic quantum potential modeling, designed to enhance the efficiency of quantum computation by autonomously adjusting quantum system parameters in response to evolving quantum potential landscapes. The algorithm leverages a computationally efficient method to simulate the dynamic behavior of the potential, dynamically adjusting parameters to minimize errors and maximize performance. We propose a framework that adapts to the inherent variability of the potential, offering a significant improvement over static optimization techniques. This work addresses a critical challenge in quantum computing – the need for adaptive algorithms capable of handling complex potential environments – with a focus on practical implementation and demonstrable performance gains.

DOI: 10.5281/zenodo.22152720

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