Aarhus University Seal

An artificial spiking quantum neuron

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Standard

An artificial spiking quantum neuron. / Kristensen, Lasse Bjørn; Degroote, Matthias; Wittek, Peter et al.

In: npj Quantum Information, Vol. 7, No. 1, 59, 2021.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Harvard

Kristensen, LB, Degroote, M, Wittek, P, Aspuru-Guzik, A & Zinner, NT 2021, 'An artificial spiking quantum neuron', npj Quantum Information, vol. 7, no. 1, 59. https://doi.org/10.1038/s41534-021-00381-7

APA

Kristensen, L. B., Degroote, M., Wittek, P., Aspuru-Guzik, A., & Zinner, N. T. (2021). An artificial spiking quantum neuron. npj Quantum Information, 7(1), [59]. https://doi.org/10.1038/s41534-021-00381-7

CBE

Kristensen LB, Degroote M, Wittek P, Aspuru-Guzik A, Zinner NT. 2021. An artificial spiking quantum neuron. npj Quantum Information. 7(1):Article 59. https://doi.org/10.1038/s41534-021-00381-7

MLA

Kristensen, Lasse Bjørn et al. "An artificial spiking quantum neuron". npj Quantum Information. 2021. 7(1). https://doi.org/10.1038/s41534-021-00381-7

Vancouver

Kristensen LB, Degroote M, Wittek P, Aspuru-Guzik A, Zinner NT. An artificial spiking quantum neuron. npj Quantum Information. 2021;7(1). 59. https://doi.org/10.1038/s41534-021-00381-7

Author

Kristensen, Lasse Bjørn ; Degroote, Matthias ; Wittek, Peter et al. / An artificial spiking quantum neuron. In: npj Quantum Information. 2021 ; Vol. 7, No. 1.

Bibtex

@article{88d4ed58bcba424797d6e514e282cbaa,
title = "An artificial spiking quantum neuron",
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.",
author = "Kristensen, {Lasse Bj{\o}rn} and Matthias Degroote and Peter Wittek and Al{\'a}n Aspuru-Guzik and Zinner, {Nikolaj T.}",
note = "Publisher Copyright: {\textcopyright} 2021, The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",
year = "2021",
doi = "10.1038/s41534-021-00381-7",
language = "English",
volume = "7",
journal = "Quantum Information & Computation",
issn = "1533-7146",
publisher = "Rinton Press, Inc.",
number = "1",

}

RIS

TY - JOUR

T1 - An artificial spiking quantum neuron

AU - Kristensen, Lasse Bjørn

AU - Degroote, Matthias

AU - Wittek, Peter

AU - Aspuru-Guzik, Alán

AU - Zinner, Nikolaj T.

N1 - Publisher Copyright: © 2021, The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85104227592&partnerID=8YFLogxK

U2 - 10.1038/s41534-021-00381-7

DO - 10.1038/s41534-021-00381-7

M3 - Journal article

AN - SCOPUS:85104227592

VL - 7

JO - Quantum Information & Computation

JF - Quantum Information & Computation

SN - 1533-7146

IS - 1

M1 - 59

ER -