Quantum Chaotic Generator and Approximation of Multivariate Functions

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper explores the potential of quantum chaotic generators (QCGs) for efficient approximation of multivariate functions and their application in quantum numerical simulations. The core claim is that the inherent randomness of a QCG can be leveraged to generate high-dimensional chaotic sequences, which can then be utilized as approximate samples for multivariate functions. The proposed mechanism relies on quantum measurements and entanglement to construct the QCG, and subsequently, the generated chaotic sequences are used as proxies for evaluating complex multivariate functions. This approach offers a novel method for numerical computation, particularly relevant within the context of quantum computing. The theoretical framework presented here outlines the design principles and potential benefits of this strategy, emphasizing its role in accelerating and potentially enhancing numerical simulations of various problems. We demonstrate the feasibility of this method through a theoretical analysis, focusing on the key aspects of sequence generation and function approximation. The ultimate goal is to provide a foundational understanding for future research in this emerging field.

DOI: 10.5281/zenodo.22220931

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