Neuromorphic quantum computing: A survey and future directions in evolutionary and gradient-based learning strategies for quantum spiking neural networks

Abstract

The convergence of quantum computing and neuromorphic engineering provides an important context for addressing the energy efficiency bottlenecks of classical deep learning and the noise constraints of Noisy Intermediate-Scale Quantum (NISQ) devices. Quantum Spiking Neural Networks (QSNNs), which integrate the biological plausibility of Spiking Neural Networks (SNNs) with the high-dimensional state space of quantum systems, have emerged as a promising paradigm for quantum-neuromorphic learning. However, the field faces significant theoretical and algorithmic challenges, primarily the “encoding mismatch” between discrete spikes and continuous quantum states, and the “double non-differentiabili..