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Quantum-Accelerated Chaos Metric Estimation

Jincheng Zhang

Published Sep 5, 2026• 0 citations
Publisher

Abstract

This paper investigates the potential of quantum algorithms to accelerate the estimation of chaotic metric functions. Chaos theory, characterized by unpredictable and seemingly random fluctuations, presents significant challenges for numerical analysis due to the sensitivity of solutions to small perturbations. Traditional methods often struggle with the computational complexity of accurately determining the metric of a chaotic system. We propose a novel quantum algorithm designed to address this challenge, leveraging quantum sampling and analysis techniques to achieve a substantial speedup compared to classical approaches. The core mechanism focuses on exploiting quantum superposition and entanglement to efficiently explore the vast parameter space of potential metric functions, ultimately minimizing the computational burden associated with chaotic metric estimation. This work explores the theoretical and potential practical implications of quantum acceleration in this critical domain.

DOI
10.5281/zenodo.22323959

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