An artificial spiking quantum neuron

Abstract 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, architectures often run on custom-designed neuromorphic hardware, but, despite attractive properties, these implementations been limited to digital systems. We describe artificial quantum neuron that relies dynamical evolution two easy implement Hamiltonians subsequent local measurements. The architecture allows exploiting complex amplitudes back-action from measurements influence input. This approach learning protocols is advantageous case input output system are both states. demonstrate this through classification Bell pairs which can be seen a certification protocol. Stacking introduced elementary building blocks into larger combines spatiotemporal features network with non-local correlations across graph.