Title: Quantum-Inspired Neural Networks (QINNs) - Adaptive Feature Extraction

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum mechanics, a cornerstone of modern physics, describes the behavior of matter and energy at the atomic and subatomic levels. It's often described through phenomena like superposition, entanglement, and quantization, where the system's state is described by a set of quantum states. These concepts have inspired researchers to develop new computational paradigms, particularly in the field of machine learning. This paper investigates the potential of Quantum-Inspired Neural Networks (QINNs) to address limitations in current neural network architectures, specifically focusing on the efficient and robust extraction of features through a novel, adaptive approach. We propose a new neural network architecture predicated on mimicking the intricate patterns observed in quantum systems, aiming to enhance generalization capabilities. This work will explore the fundamental principles behind QINNs and detail the proposed methodology for achieving improved performance through feature extraction.

DOI: 10.5281/zenodo.22639223

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