An artificial spiking quantum neuron

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

doi:10.1038/s41534-021-00381-7

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.

Highlights

As Moore’s law slows down[1], increased attention has been put towards alternative models for solving computationally hard problems and analyzing the ever growing stream of data[2,3]
We have presented a set of building blocks that detects properties represented by amplitudes {ai} and {bi} in the Bell-state basis, the probability of measuring j"i in the output will be given by of two-qubit inputs and encodes these properties in a binary and coherent way into the state of an output qubit
One thing to note is that the design of the structure in Fig. 3 is motivated by a desire for a one-step forward propagation of information between the layers of qubits, in analogy to the propagation of information in artificial classical neural networks

Summary

Introduction

As Moore’s law slows down[1], increased attention has been put towards alternative models for solving computationally hard problems and analyzing the ever growing stream of data[2,3]. When combined with this subsequent unconditional phase gate, the dynamical evolution induced by the Hamiltonian in (1) is to coherently detect the parity of the number of excitations of the input, and to encode this information in the output spin, i.e., in conventional Bell-state notation: jΨ ± ij#i À!

Results

Conclusion