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Title: Predictive Algorithmic Complexity Analysis for Complex Systems

Jincheng Zhang

Published Sep 6, 2026• 0 citations
Publisher

Abstract

This paper introduces a novel methodology for estimating the computational complexity of complex algorithms, particularly machine learning models, across a broad spectrum of input sizes and data distributions. Traditional complexity analysis methods often rely on approximations, limiting their applicability to specific scenarios. We propose a framework leveraging variational quantum computation (VQC) to simulate the evolution of algorithms on different computational scales, providing a significantly more accurate and scalable assessment of complexity. The core mechanism involves constructing a dynamic simulation model that captures the interplay between algorithm structure, data characteristics, and the inherent complexity of the underlying computation. This approach offers the potential to dramatically improve the efficiency and reliability of complexity analysis, particularly for computationally intensive algorithms. The paper details the architecture of the VQC simulation, explores its strengths and limitations, and presents preliminary results demonstrating its effectiveness in estimating complexity for several benchmark problems. The goal is to provide a robust and adaptable method for tackling the challenges posed by the increasing size and complexity of modern computational systems.

DOI
10.5281/zenodo.22514971

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