Spiking neuromorphic chip learns entangled quantum states

Czischek, Stefanie; Baumbach, Andreas; Billaudelle, Sebastian; Cramer, Benjamin; Kades, Lukas; Pawlowski, Jan M.; Oberthaler, Markus; Schemmel, Johannes; Petrovici, Mihai A.; Gasenzer, Thomas; Gärttner, Martin

doi:10.21468/SciPostPhys.12.1.039

SciPost Physics

Spiking neuromorphic chip learns entangled quantum states

Stefanie Czischek, Andreas Baumbach, Sebastian Billaudelle, Benjamin Cramer, Lukas Kades, Jan M. Pawlowski, Markus K. Oberthaler, Johannes Schemmel, Mihai A. Petrovici, Thomas Gasenzer, Martin Gärttner

SciPost Phys. 12, 039 (2022) · published 26 January 2022

doi: 10.21468/SciPostPhys.12.1.039
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Abstract

The approximation of quantum states with artificial neural networks has gained a lot of attention during the last years. Meanwhile, analog neuromorphic chips, inspired by structural and dynamical properties of the biological brain, show a high energy efficiency in running artificial neural-network architectures for the profit of generative applications. This encourages employing such hardware systems as platforms for simulations of quantum systems. Here we report on the realization of a prototype using the latest spike-based BrainScaleS hardware allowing us to represent few-qubit maximally entangled quantum states with high fidelities. Bell correlations of pure and mixed two-qubit states are well captured by the analog hardware, demonstrating an important building block for simulating quantum systems with spiking neuromorphic chips.

TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhys.12.1.039
TI  - Spiking neuromorphic chip learns entangled quantum states
PY  - 2022/01/26
UR  - https://scipost.org/SciPostPhys.12.1.039
JF  - SciPost Physics
JA  - SciPost Phys.
VL  - 12
IS  - 1
SP  - 039
A1  - Czischek, Stefanie
AU  - Baumbach, Andreas
AU  - Billaudelle, Sebastian
AU  - Cramer, Benjamin
AU  - Kades, Lukas
AU  - Pawlowski, Jan M.
AU  - Oberthaler, Markus
AU  - Schemmel, Johannes
AU  - Petrovici, Mihai A.
AU  - Gasenzer, Thomas
AU  - Gärttner, Martin
AB  - The approximation of quantum states with artificial neural networks has gained a lot of attention during the last years. Meanwhile, analog neuromorphic chips, inspired by structural and dynamical properties of the biological brain, show a high energy efficiency in running artificial neural-network architectures for the profit of generative applications. This encourages employing such hardware systems as platforms for simulations of quantum systems. Here we report on the realization of a prototype using the latest spike-based BrainScaleS hardware allowing us to represent few-qubit maximally entangled quantum states with high fidelities. Bell correlations of pure and mixed two-qubit states are well captured by the analog hardware, demonstrating an important building block for simulating quantum systems with spiking neuromorphic chips.
ER  -

@Article{10.21468/SciPostPhys.12.1.039,
	title={{Spiking neuromorphic chip learns entangled quantum states}},
	author={Stefanie Czischek and Andreas Baumbach and Sebastian Billaudelle and Benjamin Cramer and Lukas Kades and Jan M. Pawlowski and Markus K. Oberthaler and Johannes Schemmel and Mihai A. Petrovici and Thomas Gasenzer and Martin Gärttner},
	journal={SciPost Phys.},
	volume={12},
	pages={039},
	year={2022},
	publisher={SciPost},
	doi={10.21468/SciPostPhys.12.1.039},
	url={https://scipost.org/10.21468/SciPostPhys.12.1.039},
}

Cited by 6

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¹ ² Stefanie Czischek,
¹ ³ Andreas Baumbach,
¹ Sebastian Billaudelle,
¹ Benjamin Cramer,
¹ Lukas Kades,
¹ Jan M. Pawlowski,
¹ Markus Oberthaler,
¹ Johannes Schemmel,
¹ ³ Mihai A. Petrovici,
¹ Thomas Gasenzer,
¹ Martin Gärttner

Funders for the research work leading to this publication