自适应量子计算的混沌控制

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents a novel approach to quantum computation utilizing adaptive chaos control. Traditional chaos control methods often rely on fixed parameters, limiting the potential for optimized performance. We propose a system that employs machine learning to dynamically adjust the parameters of a chaotic control algorithm, resulting in improved stability and efficiency. The core mechanism involves learning a mapping between quantum state evolution and control parameters, enabling automated optimization of the chaos control process. The paper details the design of the adaptive control strategy, its implementation, and the resulting performance improvements observed through simulations. We demonstrate that this approach offers a significant advancement over traditional methods, addressing the limitations of fixed-parameter chaos control.

DOI: 10.5281/zenodo.22199126

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