# An Artificial Spiking Quantum Neuron

## TL;DR

This paper introduces a novel artificial quantum spiking neuron architecture that leverages Hamiltonian dynamics and quantum measurements, enabling quantum state classification and potential advantages in quantum neural networks.

## Contribution

It presents the first design of a quantum spiking neuron using Hamiltonian evolution and measurements, integrating quantum correlations into neural network models.

## Key findings

- Demonstrated classification of Bell pairs as a quantum certification protocol
- Proposed a scalable architecture combining spiking neural features with quantum correlations
- Showed potential for quantum neural networks leveraging non-local quantum effects

## 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, 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.