Quantum Machine Learning (QML) 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.
Researchers have introduced 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.
They used 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.
They applied these models to the MNIST, Fashion-MNIST, and KMNIST datasets for a full comparison with other classical and quantum models. They found that the proposed models perform competitively, with comparative accuracy, with efficient scaling and fast computation in classical simulation as well as on quantum devices.
npj Quantum Information, Published online: 02 December 2024; doi:10.1038/s41534-024-00921-x
