  Artificial spiking neural networks have found applications in areas where the
temporal nature of activation offers an advantage, such as time series
prediction and signal processing. To improve their efficiency, spiking
architectures often run on custom-designed neuromorphic hardware, but, despite
their attractive properties, these implementations have been limited to digital
systems. We describe an artificial quantum spiking neuron that relies on the
dynamical evolution of two easy to implement Hamiltonians and subsequent local
measurements. The architecture allows exploiting complex amplitudes and
back-action from measurements to influence the input. This approach to learning
protocols is advantageous in the case where the input and output of the system
are both quantum states. We demonstrate this through the classification of Bell
pairs which can be seen as a certification protocol. Stacking the introduced
elementary building blocks into larger networks combines the spatiotemporal
features of a spiking neural network with the non-local quantum correlations
across the graph.
