International Conference on Algorithms, Network, and Communication Technology (ICANCT)

Optimization of quantum neuron based on repeat-until-success circuit

Fanghua Gao, Xianglong Yu, Zhimin Wang

Published Feb 17, 2026• 0 citations

Abstract

Although the Repeat-Until-Success (RUS) neuron is functionally analogous to the activation function in classical neural networks, its practical application still faces limitations such as high operational complexity and the requirement for binary input states. To address these issues, this paper optimizes the RUS neuron. By merging specific quantum gate sequences into CNOT gates, we propose an optimized RUS neuron, which significantly reduces quantum resource overhead while maintaining functional integrity. By conducting an in-depth analysis of their dynamic behavior under both binary and superposition input states, we reveal the characteristic mechanisms of their activation-like functionality. To validate the practicality of the optimized RUS neuron, we design two typical application scenarios: constructing a quantum perceptron model to solve the XOR problem and developing a Hopfield neural network for image restoration. Simulation experiments demonstrate that the proposed quantum perceptron achieves 100% classification accuracy on the XOR problem, while the Hopfield network successfully reconstructs occluded images, fully confirming the practical feasibility and technical superiority of RUS neurons in quantum neural networks.

DOI: 10.1117/12.3109413

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