• Medientyp: E-Artikel
  • Titel: Spiking neuromorphic chip learns entangled quantum states
  • Beteiligte: Czischek, Stefanie; Baumbach, Andreas; Billaudelle, Sebastian; Cramer, Benjamin; Kades, Lukas; Pawlowski, Jan M.; Oberthaler, Markus; Schemmel, Johannes; Petrovici, Mihai A.; Gasenzer, Thomas; Gärttner, Martin
  • Erschienen: Stichting SciPost, 2022
  • Erschienen in: SciPost Physics
  • Sprache: Nicht zu entscheiden
  • DOI: 10.21468/scipostphys.12.1.039
  • ISSN: 2542-4653
  • Schlagwörter: General Physics and Astronomy
  • Entstehung:
  • Anmerkungen:
  • Beschreibung: <jats:p>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.</jats:p>
  • Zugangsstatus: Freier Zugang