Quantum-Inspired Neural Networks with Entangled Node Representations

Jincheng Zhang

Published Aug 28, 2026• 0 citations

Abstract

Quantum-inspired neural networks (QINNs) have emerged as a promising approach to leveraging quantum computational principles for accelerating and enhancing artificial intelligence. However, a significant limitation of existing QINNs lies in their failure to fully capture the essence of quantum entanglement, a cornerstone of quantum mechanics. This paper proposes a novel architecture, termed Entangled Node Representation (ENR) QINNs, which directly incorporates true quantum entanglement into the network structure. The core idea is to represent nodes as entangled qubits, allowing for inherent correlations between nodes that surpass the limitations of classical neural networks. We postulate that this approach will dramatically improve learning efficiency by enabling the network to explore a wider solution space and converge faster. The proposed ENR QINNs introduce a formal mathematical framework for modeling and training these networks, exploring the potential of superposition and interference effects within the network's architecture. The theoretical analysis demonstrates that the ENR QINNs possess the potential to achieve exponential speedups in computation compared to traditional neural networks, particularly in tasks involving complex correlations. We outline key considerations for the design and training of ENR QINNs, including qubit representation choices, entanglement management strategies, and optimization algorithms specifically tailored to exploit the quantum properties of the network. Further research will focus on experimental validation and scaling the architecture for real-world applications.

DOI: 10.5281/zenodo.22139749

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