Artificial Spiking Quantum Neural Networks

Within the realm of classical computing, the paradigm of artificial spiking neural networks has found a wide range of applications in settings where the temporal aspects of such networks are advantageous, such as in time-series prediction and signal analysis. In this talk, we will present a class of simple quantum spin-network models inspired by this classical paradigm, combining both an explicit neural network structure and explicit temporality through quantum evolution, and with the inherent ability to operate on quantum data as input. A set of neuron-like building blocks for such networks will be presented, and a network combining these objects into a structure capable of comparing pairs of Bell states will be proposed, a task with applications in quantum certification. Finally, a few comments on inherent properties related to the generation of entanglement and measurement back-action through these networks will be given.

The content of this talk is based on the arXiv-preprint 1907.06269