Dynamic Chaos Optimization via Quantum Potential Landscapes

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces a novel dynamic chaos optimization framework based on quantum potential landscapes. Traditional optimization algorithms often struggle with complex, non-linear systems due to the limitations of classical methods. This work leverages the unique properties of quantum potential landscapes, which exhibit dynamic and spatially varying energy states, to guide optimization processes. The core mechanism involves constructing a quantum potential landscape that dynamically adjusts to the system's state, promoting efficient solutions and minimizing errors. We demonstrate the effectiveness of this approach through simulations of a chaotic system, showcasing improvements in performance metrics such as convergence speed and solution quality. The potential landscape provides a mechanism for self-organization and adaptation, leading to enhanced optimization outcomes. The study also explores the impact of varying the landscape's parameters on the optimization process. The paper concludes with a discussion of future research directions and potential applications of this quantum-inspired optimization technique.

DOI: 10.5281/zenodo.22639612

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