Quantum machine learning is in a period of rapid development and discovery, however it still lacks the resources and diversity of computational models of its classical complement. With the growing difficulties of classical models requiring extreme hardware and power solutions, and quantum models being limited by noisy intermediate-scale quantum (NISQ) hardware, there is an emerging opportunity to solve both problems together. Here we introduce a new software model for quantum neuromorphic computing -- a quantum leaky integrate-and-fire (QLIF) neuron, implemented as a compact high-fidelity quantum circuit, requiring only 2 rotation gates and no CNOT gates. We use these neurons as building blocks in the construction of a quantum spiking neural network (QSNN), and a quantum spiking convolutional neural network (QSCNN), as the first of their kind. We apply these models to the MNIST, Fashion-MNIST, and KMNIST datasets for a full comparison with other classical and quantum models. We find that the proposed models perform competitively, with comparative accuracy, with efficient scaling and fast computation in classical simulation as well as on quantum devices.

翻译：量子机器学习正处于快速发展和发现阶段，然而相较于经典计算模型，其仍缺乏足够的计算资源与模型多样性。随着经典模型对极端硬件与功耗解决方案的需求日益增长，以及量子模型受限于噪声中等规模量子（NISQ）硬件，一个同时解决这两大问题的新机遇正在浮现。本文提出了一种量子神经形态计算的新软件模型——量子泄漏积分发放（QLIF）神经元，该模型以紧凑的高保真量子电路实现，仅需2个旋转门且无需CNOT门。我们以这些神经元为基本构建模块，首次构建了量子脉冲神经网络（QSNN）与量子脉冲卷积神经网络（QSCNN）。我们将这些模型应用于MNIST、Fashion-MNIST及KMNIST数据集，以全面对比其他经典与量子模型。研究发现，所提出的模型在经典模拟与量子设备上均表现出竞争力，具有相当的分类精度、高效的扩展性与快速的计算能力。