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 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.

9 pages, 7 figures

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• Early Fault-Tolerant Quantum Algorithms in Practice: Application to Ground-State Energy Estimation
• Splitting and Parallelizing of Quantum Convolutional Neural Networks for Learning Translationally Symmetric Data
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• Permutation-equivariant quantum convolutional neural networks
• Supervised Learning Guarantee for Quantum AdaBoost
• Quantum phase classification via partial tomography-based quantum hypothesis testing
• Numerically efficient unitary evolution for Hamiltonians beyond nearest-neighbors