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Verfasst von:Baumbach, Andreas [VerfasserIn]   i
 Klassert, Robert [VerfasserIn]   i
 Czischek, Stefanie [VerfasserIn]   i
 Gärttner, Martin [VerfasserIn]   i
 Petrovici, Mihai A. [VerfasserIn]   i
Titel:Quantum many-body states
Titelzusatz:a novel neuromorphic application
Verf.angabe:Andreas Baumbach, Robert Klassert, Stefanie Czischek, Martin Gärttner, Mihai A. Petrovici
E-Jahr:2022
Jahr:03 May 2022
Umfang:3 S.
Fussnoten:Gesehen am 17.10.2022
Titel Quelle:Enthalten in: Okandan, MuratNeuro-Inspired Computational Elements Conference
Ort Quelle:New York,NY,United States : Association for Computing Machinery, 2022
Jahr Quelle:2022
Band/Heft Quelle:(2022), Seite 104-106
ISBN Quelle:978-1-4503-9559-5
Abstract:Emergent phenomena in condensed matter physics, such as superconductivity, are rooted in the interaction of many quantum particles. These phenomena remain poorly understood in part due to the computational demands of their simulation. In recent years variational representations based on artificial neural networks, so called neural quantum states (NQS), have been shown to be efficient, ie. sub-exponentially scaling, representations. However, the computational complexity of such representations scales not only with the size of the physical system, but also with the size of the neural network. In this work, we use the analog neuromorphic BrainScaleS-2 platform to implement probabilistic representations of two particular types of quantum states. The physical nature of the neuromorphic system enforces an inherent parallelism of the compuation, rendering the emulation time independent of the used network size. We show the effectiveness of our scheme in two settings: First, we consider a hallmark test for ”quantumness” by representing a quantum state that violates the classical bounds of the Bell inequality. Second, we show that we can represent the large class of stoquastic quantum states with fidelities above 98% for moderate system sizes. This offers a novel application for spike-based neuromorphic hardware which departs from the more traditional neuroscience-inspired use cases.
DOI:doi:10.1145/3517343.3517379
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext: https://doi.org/10.1145/3517343.3517379
 DOI: https://doi.org/10.1145/3517343.3517379
Datenträger:Online-Ressource
Sprache:eng
K10plus-PPN:181899402X
Verknüpfungen:→ Sammelwerk

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