Quantum Neural Networks via Multi-Body Entanglement Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper investigates the potential of quantum neural networks (QNNs) by focusing on optimizing multi-body entanglement within the network architecture. The core claim is that strategic manipulation of entanglement between multiple nodes in a neural network can significantly enhance computational capabilities and improve information encoding efficiency. The proposed mechanism involves designing an algorithm to dynamically control the entanglement strength between nodes. This allows for the realization of more efficient parallel computation and enhanced information encoding. We explore theoretical frameworks and mathematical formulations to demonstrate the potential advantages of this approach compared to classical neural networks. The research highlights a novel pathway toward constructing quantum-enhanced neural network models by leveraging the unique properties of quantum entanglement. The primary focus is on the mathematical modeling and optimization strategies required to effectively harness entanglement for QNN performance.

DOI: 10.5281/zenodo.22196546

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