Quantum many-body states

• Verfasst von: Baumbach, Andreas [VerfasserIn]
• Verfasst von: Klassert, Robert [VerfasserIn]
• Verfasst von: Czischek, Stefanie [VerfasserIn]
• Verfasst von: Gärttner, Martin [VerfasserIn]
• Verfasst von: Petrovici, Mihai A. [VerfasserIn]
• 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
• DOI: doi:10.1145/3517343.3517379
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 181899402X

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.