Deep Spiking Quantum Neural Network for Noisy Image Classification.

Recently, quantum machine learning has been applied to stochastic-based modelling, promising that the inherent uncertainty in quantum computing will be a significant advantage, driving neuromorphic computing research to new heights. Spiking Neural Networks (SNNs) and their neuromorphic are gaining popularity due to their inherent ability to process spatial and temporal data. However, learning the interconnection weights is daunting due to the inherent stochastic characteristics of neuron signals and the inherent non-differentiable spike events in classical SNN. This paper introduces a supervised Deep Spiking Quantum Neural Network (DSQ-Net) using a hybrid quantum-classical architecture having the merits of amplitude encoding in a dressed quantum layer. A novel attempt has been made to obviate the challenges in training a classical deep SNN, assisted by a Variational Quantum Circuit (VQC) in the proposed hybrid quantum-classical framework. The DSQ-Net has undergone thorough validation and benchmarking procedures using the PennyLane Quantum Simulator and the limited volume real IBM Quantum hardware. The experiments have been conducted on images from the MNIST, FashionMNIST, KMNIST, and CIFAR-10 datasets. Classification accuracy has been reported in the high nineties for unseen noisy test images using the proposed DSQ-Net model on the quantum simulator. It outperforms its classical counterpart (Deep Spiking Neural Networks), shallow Random Quantum Neural Networks (RQNN), Quantum Superposition-inspired Spiking Neural Networks (SQIN), ResNet18, and AlexNet. The PyTorch implementation of DSQ-Net is made available on Github(1).