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arXiv · 2208.08748

Quantum phase detection generalisation from marginal quantum neural network models

Saverio Monaco·Oriel Kiss·Antonio Mandarino·Sofia Vallecorsa·Michele Grossi
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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 Letter, using quantum convolutional neural networks, we overcome this limit by determining the phase diagram of a model where analytical solutions are lacking, 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 ferromagnetic, paramagnetic and antiphase, showing that the whole phase diagram can be reproduced.

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Saverio Monaco, Oriel Kiss, Antonio Mandarino, Sofia Vallecorsa, Michele Grossi. 2022-08-18. Quantum phase detection generalisation from marginal quantum neural network models. https://doi.org/10.1103/physrevb.107.l081105

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