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An artificial spiking quantum neuron

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  • Lasse Bjørn Kristensen
  • ,
  • Matthias Degroote, University of Toronto, Harvard University
  • ,
  • Peter Wittek, University of Toronto, Creative Destruction Lab, Vector Institute for Artificial Intelligence, Perimeter Institute for Theoretical Physics
  • ,
  • Alán Aspuru-Guzik, University of Toronto, Harvard University, Vector Institute for Artificial Intelligence, Zapata Computing Inc., Canadian Institute For Advanced Research
  • ,
  • Nikolaj T. Zinner

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.

Original languageEnglish
Article number59
Journalnpj Quantum Information
Number of pages7
Publication statusPublished - 2021

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