Bulletin of the American Physical Society
APS March Meeting 2021
Volume 66, Number 1
Monday–Friday, March 15–19, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session B21: Machine Learning for Quantum Matter II
11:30 AM–2:18 PM,
Monday, March 15, 2021
Sponsoring
Units:
DCOMP GDS DMP
Chair: Mohamed Hibat-Allah, University of Waterloo
Abstract: B21.00003 : Spiking Neuromorphic Chip Encodes Quantum Entanglement Correlations
11:54 AM–12:06 PM
Live
Presenter:
Stefanie Czischek
(Department of Physics and Astronomy, University of Waterloo)
Authors:
Stefanie Czischek
(Department of Physics and Astronomy, University of Waterloo)
Andreas Baumbach
(Kirchhoff Institute for Physics, Heidelberg University)
Sebastian Billaudelle
(Kirchhoff Institute for Physics, Heidelberg University)
Benjamin Cramer
(Kirchhoff Institute for Physics, Heidelberg University)
Lukas Kades
(Institute for Theoretical Physics, Heidelberg University)
Jan M. Pawlowski
(Institute for Theoretical Physics, Heidelberg University)
Johannes Schemmel
(Kirchhoff Institute for Physics, Heidelberg University)
Markus Oberthaler
(Kirchhoff Institute for Physics, Heidelberg University)
Mihai Petrovici
(Department of Physiology, University of Bern)
Thomas Gasenzer
(Kirchhoff Institute for Physics, Heidelberg University)
Martin Gaerttner
(Kirchhoff Institute for Physics, Heidelberg University)
Here we report on the realization of a prototype using the spike-based BrainScaleS hardware developed in the context of European’s Human Brain Project (HBP). This chip realizes fast analog dynamics to boost computationally expensive tasks.
The probabilistic implementation of quantum states is achieved through Bayesian sampling by the spiking neurons. Furthermore, a specific Hebbian learning scheme exploits the hardware speed and allows for a variety of network topologies. Training the hardware-encoded network to represent maximally entangled quantum states of up to four qubits reaches high fidelities. Extracted Bell correlations for two-qubit states convey that non-classical features are captured by the analog hardware, demonstrating the feasibility of simulating quantum systems with spiking neuromorphic chips.
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