A Quantum Leaky Integrate-and-Fire Spiking Neuron and Network

TL;DR

This paper introduces a novel quantum neuromorphic computing model with a quantum leaky integrate-and-fire neuron, enabling the construction of quantum spiking neural networks that perform competitively on standard datasets.

Contribution

It presents the first implementation of quantum spiking neural networks and a quantum leaky integrate-and-fire neuron with minimal quantum gates, advancing quantum neuromorphic computing.

Findings

Quantum SNNs perform competitively on MNIST datasets.

The proposed models scale efficiently and compute quickly.

High-fidelity quantum circuits enable practical quantum neuromorphic models.

Abstract

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.