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.

该研究引入了一种名为 PPF-QSNN 的新型架构，通过将数据集信息同时输入脉冲神经网络和变分量子电路，并按照各自的贡献比例合并输出，系统评估了各种 PPF-QSNN 参数对图像分类网络性能的影响，旨在确定最佳配置。MNIST 数据集的数字结果明确表明，我们提出的 PPF-QSNN 在准确度、损失和稳健性等指标上优于现有的脉冲神经网络和串行量子神经网络，为 HQCNN 的发展和应用奠定了基础，推动了人工智能计算中量子优势的应用。

并行比例融合的脉冲量子神经网络优化图像分类

Parallel Proportional Fusion of Spiking Quantum Neural Network for  Optimizing Image Classification

A limited set of tools exist for assessing whether the behavior of quantum
machine learning models diverges from conventional models, outside of abstract
or theoretical settings. We present a systematic application of explainable
artificial intelligence techniques to analyze synthetic data generated from a
hybrid quantum-classical neural network adapted from twenty different
real-world data sets, including solar flares, cardiac arrhythmia, and speech
data. Each of these data sets exhibits varying degrees of complexity and class
imbalance. We benchmark the quantum-generated data relative to state-of-the-art
methods for mitigating class imbalance for associated classification tasks. We
leverage this approach to elucidate the qualities of a problem that make it
more or less likely to be amenable to a hybrid quantum-classical generative
model.

使用可解释的人工智能技术对从 20 个真实数据集生成的合成数据进行分析，探究了一种混合量子 - 经典神经网络生成模型的特点和适用性，并与处理类别不平衡的最先进方法进行比较。

通过不平衡数据分类基准评估量子生成模型

Evaluating quantum generative models via imbalanced data classification  benchmarks

While quantum architectures are still under development, when available, they
will only be able to process quantum data when machine learning algorithms can
only process numerical data. Therefore, in the issues of classification or
regression, it is necessary to simulate and study quantum systems that will
transfer the numerical input data to a quantum form and enable quantum
computers to use the available methods of machine learning. This material
includes the results of experiments on training and performance of a hybrid
quantum-classical neural network developed for the problem of classification of
handwritten digits from the MNIST data set. The comparative results of two
models: classical and quantum neural networks of a similar number of training
parameters, indicate that the quantum network, although its simulation is
time-consuming, overcomes the classical network (it has better convergence and
achieves higher training and testing accuracy).

研究了用于手写数字 MNIST 数据集分类问题的混合量子 - 经典神经网络的性能，结果表明虽然模拟时间较长，但是该模型优于传统网络，达到更好的收敛性和高的训练和测试精度。