Quantum machine learning of graph-structured data
- verfasst von
- Kerstin Beer, Megha Khosla, Julius Köhler, Tobias J. Osborne, Tianqi Zhao
- Abstract
Graph structures are ubiquitous throughout the natural sciences. Here we develop an approach that exploits the quantum source's graph structure to improve learning via an arbitrary quantum neural network (QNN) ansatz. In particular, we devise and optimize a self-supervised objective to capture the information-theoretic closeness of the quantum states in the training of a QNN. Numerical simulations show that our approach improves the learning efficiency and the generalization behavior of the base QNN. On a practical note, scalable quantum implementations of the learning procedure described in this paper are likely feasible on the next generation of quantum computing devices.
- Organisationseinheit(en)
-
Institut für Theoretische Physik
SFB 1227: Designte Quantenzustände der Materie (DQ-mat)
- Externe Organisation(en)
-
Macquarie University
Delft University of Technology
- Typ
- Artikel
- Journal
- Physical Review A
- Band
- 108
- ISSN
- 2469-9926
- Publikationsdatum
- 10.07.2023
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Atom- und Molekularphysik sowie Optik
- Elektronische Version(en)
-
https://doi.org/10.48550/arXiv.2103.10837 (Zugang:
Offen)
https://doi.org/10.1103/PhysRevA.108.012410 (Zugang: Geschlossen)
