Quantum phase detection generalisation from marginal quantum neural network models

Abstract: Quantum machine learning offers a promising advantage in extracting information about quantum states, e.g. phase diagram. However, access to training labels is a major bottleneck for any supervised approach, preventing getting insights about new physics. In this work, using quantum convolutional neural networks, we overcome this limit by determining the phase diagram of a model where no analytical solutions are known, by training only on marginal points of the phase diagram, where integrable models are represented.
More specifically, we consider the Axial Next Nearest Neighbor Ising (ANNNI) Hamiltonian, which possesses a ferro-magnetic, para-magnetic and antiphase, showing that the whole phase diagram can be reproduced.

Download paper here

Recommended citation:

@article{Monaco_2023,
   title={Quantum phase detection generalization from marginal quantum neural network models},
   volume={107},
   ISSN={2469-9969},
   url={http://dx.doi.org/10.1103/PhysRevB.107.L081105},
   DOI={10.1103/physrevb.107.l081105},
   number={8},
   journal={Physical Review B},
   publisher={American Physical Society (APS)},
   author={Monaco, Saverio and Kiss, Oriel and Mandarino, Antonio and Vallecorsa, Sofia and Grossi, Michele},
   year={2023},
   month=2}