  The classification of phase transitions is a central and challenging task in
condensed matter physics. Typically, it relies on the identification of order
parameters and the analysis of singularities in the free energy and its
derivatives. Here, we propose an alternative framework to identify quantum
phase transitions, employing both unsupervised and supervised machine learning
techniques. Using the axial next-nearest neighbor Ising (ANNNI) model as a
benchmark, we show how unsupervised learning can detect three phases
(ferromagnetic, paramagnetic, and a cluster of the antiphase with the floating
phase) as well as two distinct regions within the paramagnetic phase. Employing
supervised learning we show that transfer learning becomes possible: a machine
trained only with nearest-neighbour interactions can learn to identify a new
type of phase occurring when next-nearest-neighbour interactions are
introduced. All our results rely on few and low dimensional input data (up to
twelve lattice sites), thus providing a computational friendly and general
framework for the study of phase transitions in many-body systems.
