  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.
