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Parallel proportional fusion of a spiking quantum neural network for optimizing image classification

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Abstract

The recent emergence of the hybrid quantum-classical neural network (HQCNN) architecture has garnered considerable attention because of the potential advantages associated with integrating quantum principles to enhance various facets of machine learning algorithms and computations. However, the current investigated serial structure of HQCNN, wherein information sequentially passes from one network to another, often imposes limitations on the trainability and expressivity of the network. In this study, we introduce a novel architecture termed parallel proportional fusion of spiking and quantum neural networks (PPF-SQNN). The dataset information is simultaneously fed into both the spiking neural network and the variational quantum circuits, with the outputs amalgamated in proportion to their individual contributions. We systematically assess the impact of diverse PPF-SQNN parameters on network performance for image classification, aiming to identify the optimal configuration. On three datasets for image classification tasks, the final classification accuracy reached 98.2%, 99.198%, and 97.921%, respectively, with loss values all below 0.2, outperforming the compared serial networks. In noise testing, it also demonstrates good classification performance even under noise intensities of 0.9 Gaussian and uniform noise. This study introduces a novel and effective amalgamation approach for HQCNN, laying the groundwork for the advancement and application of quantum advantages in artificial intelligence computations.

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Data Availability

Data sharing not applicable.

Code Availability

The PyTorch implementation of PPF-SQNN is available on Github at https://github.com/shenkg/PPF-SQNN

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Funding

This work was partly supported by the National Natural Science Foundation of China (Grant Nos. 62201005, 62274002, 62004001, 62004001, 62304001), the Anhui Provincial Natural Science Foundation under Grant No. 2308085QF213, No. 2108085MF228, and the Natural Science Research Project of Anhui Educational Committee under Grant No. 2022AH050106, 2023AH050072.

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Authors and Affiliations

  1. School of Integrated Circuits, Anhui University, Hefei, Anhui, 230601, China

    Zuyu Xu, Kang Shen, Pengnian Cai, Tao Yang, Yuanming Hu, Yunlai Zhu, Zuheng Wu, Yuehua Dai, Jun Wang & Fei Yang

  2. School of Computer and information, Anhui Normal University, Wuhu, Anhui, 241003, China

    Shixian Chen

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  1. Zuyu Xu
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  2. Kang Shen
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All authors contributed to the study conception

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Correspondence to Fei Yang.

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Xu, Z., Shen, K., Cai, P. et al. Parallel proportional fusion of a spiking quantum neural network for optimizing image classification. Appl Intell 54, 11876–11891 (2024). https://doi.org/10.1007/s10489-024-05786-3

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