  The recent emergence of the hybrid quantum-classical neural network (HQCNN)
architecture has garnered considerable attention due to 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 Quantum and Spiking Neural Networks
(PPF-QSNN). 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-QSNN parameters on network performance for
image classification, aiming to identify the optimal configuration. Numerical
results on the MNIST dataset unequivocally illustrate that our proposed
PPF-QSNN outperforms both the existing spiking neural network and the serial
quantum neural network across metrics such as accuracy, loss, and robustness.
This study introduces a novel and effective amalgamation approach for HQCNN,
thereby laying the groundwork for the advancement and application of quantum
advantage in artificial intelligent computations.
