An artificial spiking quantum neuron

Journal Article · Mon Apr 12 00:00:00 EDT 2021 · npj Quantum Information

DOI:https://doi.org/10.1038/s41534-021-00381-7· OSTI ID:1776393

Kristensen, Lasse Bjørn ; Degroote, Matthias ; Wittek, Peter; Aspuru-Guzik, Alán ; Zinner, Nikolaj T.

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.

• Sponsoring Organization: USDOE
• OSTI ID: 1776393
• Journal Information: npj Quantum Information, Journal Name: npj Quantum Information Vol. 7 Journal Issue: 1; ISSN 2056-6387
• Publisher: Nature Publishing Group
• Country of Publication: United Kingdom
• Language: English