Quantum Resonance Networks for Algorithmic Complexity Analysis

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum Resonance Networks (QRNs) represent a novel approach to algorithmic complexity analysis, leveraging the principles of entanglement and superposition to create dynamic, interconnected networks. This research explores the potential of QRNs as a tool for quantifying complexity, moving beyond traditional approximation methods and offering a fundamentally quantum-based assessment. The core mechanism involves establishing a network where nodes are interconnected through entanglement, generating patterns that are indicative of complex algorithmic behavior. This paper details the framework for QRNs, its underlying principles, and its potential impact on the field of algorithmic complexity.

DOI: 10.5281/zenodo.22519091

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