Quantum Neural Networks: Topology Optimization of Neuronal Connections

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper investigates the application of topology optimization techniques to enhance the performance of quantum neural networks (QNNs). Traditional QNN architectures often suffer from inefficiencies in information processing due to suboptimal connections between neurons. This work proposes a novel approach utilizing topology optimization algorithms to dynamically adjust the connectivity topology within a QNN, aiming to maximize computational capacity and efficiency. The core claim is that optimizing the neuronal connection topology is a key factor in improving QNN performance. The proposed mechanism involves the iterative application of a topology optimization algorithm, which iteratively modifies the network's connectivity structure based on a defined objective function. The results demonstrate the potential of this method to significantly improve QNN performance, offering a pathway to more powerful and efficient quantum computing architectures. The specific objective function utilized focuses on maximizing the fidelity of quantum state transformations while minimizing the number of connections, representing a novel approach to QNN design. Further exploration will involve investigating different optimization algorithms and network architectures to fully realize the potential of this topology-optimized QNN design.

DOI: 10.5281/zenodo.22268658

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