Details
| Original language | English |
|---|---|
| Article number | 012410 |
| Journal | Physical Review A |
| Volume | 108 |
| Issue number | 1 |
| Publication status | Published - 10 Jul 2023 |
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.
ASJC Scopus subject areas
- Physics and Astronomy(all)
- Atomic and Molecular Physics, and Optics
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In: Physical Review A, Vol. 108, No. 1, 012410, 10.07.2023.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Quantum machine learning of graph-structured data
AU - Beer, Kerstin
AU - Khosla, Megha
AU - Köhler, Julius
AU - Osborne, Tobias J.
AU - Zhao, Tianqi
N1 - Funding Information: Helpful correspondence and discussions with D. Bondarenko, T. Farrelly, P. Feldmann, A. Hahn, G. Müller, J. Hendrik Pfau, R. Salzmann, D. Scheiermann, V. Schmiesing, M. Schwiering, C. Struckmann, and R. Wolf are gratefully acknowledged. This work was supported in part by the Quantum Valley Lower Saxony, the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through SFB 1227 (DQ-mat), the RTG 1991, and DFG under Germany's Excellence Strategy EXC-2123 QuantumFrontiers Grant No. 390837967.
PY - 2023/7/10
Y1 - 2023/7/10
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85165544384&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2103.10837
DO - 10.48550/arXiv.2103.10837
M3 - Article
AN - SCOPUS:85165544384
VL - 108
JO - Physical Review A
JF - Physical Review A
SN - 2469-9926
IS - 1
M1 - 012410
ER -