Title: Quantum State-Space Hybrid Neural Networks (QSS-NN)

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum State-Space Hybrid Neural Networks (QSS-NN) presents a novel approach to artificial neural network design, leveraging the unique capabilities of quantum mechanics to enhance learning capabilities. This paper explores the integration of quantum information processing directly into the core of a neural network architecture, aiming to overcome limitations inherent in classical models. We introduce a framework predicated on the concept of quantum state-space entanglement, utilizing quantum circuits to represent input data and enable the network to learn from correlated quantum states. The core mechanism focuses on utilizing quantum coherence as a key feature for improved data representation and learning. The resulting QSS-NN architecture demonstrates significant potential for improved performance in certain complex tasks, offering a fundamentally different perspective on neural network design. This work investigates the potential of this approach through theoretical analysis and preliminary experimental validation.

DOI: 10.5281/zenodo.22491715

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