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Adaptive Complexity Measurement via Quantum-Enhanced Metrics

Jincheng Zhang

Published Aug 29, 2026• 0 citations
Publisher

Abstract

This paper investigates a novel approach to quantitatively measuring complexity within complex systems, leveraging quantum entanglement to generate a measurement sensitive to subtle correlations and emergent patterns. Traditional complexity measures often struggle to capture the intricate dynamics of these systems. This work proposes a method that harnesses the unique properties of quantum entanglement to identify and quantify these hidden structures, offering a fundamentally different perspective on complexity assessment. We demonstrate, through theoretical analysis and preliminary simulations, the potential for this quantum-enhanced metric to surpass existing methods in its ability to detect and characterize complex behavior. The paper also outlines the challenges and future directions for this research.

DOI
10.5281/zenodo.22154586

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This paper presents a novel theoretical framework for dynamic complexity computation, leveraging the unique properties of quantum entanglement to enhance the assessment of complex system computational complexity. We propose a method that establishes a direct link between complexity metrics and entanglement, enabling the precise evaluation of computational stability and guiding algorithm optimization based on this understanding. The core mechanism involves associating complexity with entanglement's inherent characteristics, utilizing entanglement for stability assessment and algorithmic optimization. This approach offers a fundamentally different perspective on complexity, moving beyond traditional statistical methods and embracing the subtle yet powerful interplay of quantum mechanics. This research aims to provide a robust and potentially transformative tool for computational complexity analysis, particularly in the design and optimization of complex algorithms.
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