Title: Quantum-Inspired Neural Networks for Enhanced Pattern Recognition

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum-inspired neural networks represent a novel approach to pattern recognition, leveraging the principles of quantum mechanics to enhance the exploration of high-dimensional spaces and the detection of subtle correlations. This paper explores the integration of quantum-inspired concepts – specifically superposition and entanglement – into neural network architectures to address the limitations of current models in complex pattern identification. We investigate the impact of these design choices on network performance across diverse datasets, demonstrating potential improvements in accuracy and robustness. This work seeks to advance the field of neural network design by introducing a fundamentally new methodology for pattern recognition.

DOI: 10.5281/zenodo.22280124

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