Quantum state assignment flows

• Verfasst von: Schwarz, Jonathan [VerfasserIn]
• Verfasst von: Cassel, Jonas [VerfasserIn]
• Verfasst von: Boll, Bastian [VerfasserIn]
• Verfasst von: Gärttner, Martin [VerfasserIn]
• Verfasst von: Albers, Peter [VerfasserIn]
• Verfasst von: Schnörr, Christoph [VerfasserIn]
• Titel: Quantum state assignment flows
• Verf.angabe: Jonathan Schwarz, Jonas Cassel, Bastian Boll, Martin Gärttner, Peter Albers and Christoph Schnörr
• E-Jahr: 2023
• Jahr: 23 August 2023
• Umfang: 36 S.
• Fussnoten: Gesehen am 13.11.2023
• Titel Quelle: Enthalten in: Entropy
• Ort Quelle: Basel : MDPI, 1999
• Jahr Quelle: 2023
• Band/Heft Quelle: 25(2023), 9, Artikel-ID 1253, Seite 1-16
• ISSN Quelle: 1099-4300
• DOI: doi:10.3390/e25091253
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: assignment flows
• Sach-SW: density matrix
• Sach-SW: information geometry
• Sach-SW: Riemannian gradient flows
• K10plus-PPN: 1870135830

Abstract

This paper introduces assignment flows for density matrices as state spaces for representation and analysis of data associated with vertices of an underlying weighted graph. Determining an assignment flow by geometric integration of the defining dynamical system causes an interaction of the non-commuting states across the graph, and the assignment of a pure (rank-one) state to each vertex after convergence. Adopting the Riemannian-Bogoliubov-Kubo-Mori metric from information geometry leads to closed-form local expressions that can be computed efficiently and implemented in a fine-grained parallel manner. Restriction to the submanifold of commuting density matrices recovers the assignment flows for categorical probability distributions, which merely assign labels from a finite set to each data point. As shown for these flows in our prior work, the novel class of quantum state assignment flows can also be characterized as Riemannian gradient flows with respect to a non-local, non-convex potential after proper reparameterization and under mild conditions on the underlying weight function. This weight function generates the parameters of the layers of a neural network corresponding to and generated by each step of the geometric integration scheme. Numerical results indicate and illustrate the potential of the novel approach for data representation and analysis, including the representation of correlations of data across the graph by entanglement and tensorization.