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.

量子机器学习正处于快速发展和发现的时期，但它仍然缺乏其经典补充的计算模型的资源和多样性。随着经典模型需要极端硬件和电源解决方案的难度越来越大，而量子模型受到嘈杂的中等规模量子 （NISQ） 硬件的限制，因此出现了同时解决这两个问题的机会。在这里，我们介绍了一种用于量子神经形态计算的新软件模型——量子泄漏集成和发射 （QLIF） 神经元，作为紧凑的高保真量子电路实现，只需要 2 个旋转门，不需要 CNOT 门。我们使用这些神经元作为构建量子脉冲神经网络 （QSNN） 的构建块，并使用量子脉冲卷积神经网络 （QSCNN） 作为同类产品中的首创。我们将这些模型应用于 MNIST 、 Fashion-MNIST 和 KMNIST 数据集，以便与其他经典和量子模型进行全面比较。我们发现，所提出的模型在经典模拟和量子设备上具有竞争力、相对准确性、高效缩放和快速计算。