Quantum phase detection generalisation from marginal quantum neural network models

• URL: http://arxiv.org/abs/2208.08748v2
• Date: Tue, 24 Jan 2023 17:15:04 GMT
• Title: Quantum phase detection generalisation from marginal quantum neural network models
• Authors: Saverio Monaco, Oriel Kiss, Antonio Mandarino, Sofia Vallecorsa and Michele Grossi
• Abstract summary: We use quantum convolutional neural networks to determine the phase diagram of a model where analytical solutions are lacking. More specifically, we consider the axial next-nearest-neighbor Ising (ANNNI) Hamiltonian, which possesses a ferromagnetic, paramagnetic and antiphase.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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