https://doi.org/10.1140/epjp/s13360-022-03587-6
Regular Article
Quantum phase recognition using quantum tensor networks
1
Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India
2
Center for Quantum Science and Technology, International Institute of Information Technology, Hyderabad, India
a
shweta.sahoo@research.iiit.ac.in
b
utkarsh.azad@research.iiit.ac.in
Received:
6
August
2022
Accepted:
11
December
2022
Published online:
26
December
2022
Machine learning (ML) has recently facilitated many advances in solving problems related to many-body physical systems. Given the intrinsic quantum nature of these problems, it is natural to speculate that quantum-enhanced machine learning will enable us to unveil even greater details than we currently have. With this motivation, this paper examines a quantum machine learning approach based on shallow variational ansatz inspired by tensor networks for supervised learning tasks. In particular, we first look at the standard image classification tasks using the Fashion-MNIST dataset and study the effect of repeating tensor network layers on ansatz’s expressibility and performance. Finally, we use this strategy to tackle the problem of quantum phase recognition for the transverse-field Ising and Heisenberg spin models in one and two dimensions, where we were able to reach 
test-set accuracies with both multi-scale entanglement renormalization ansatz (MERA) and tree tensor network (TTN) inspired parametrized quantum circuits.
© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
