  We detect the quantum phase transition of a quantum many-body system by
mapping the observed results of the quantum state onto a neural network. In the
present study, we utilized the simplest case of a quantum many-body system,
namely a one-dimensional chain of Ising spins with the transverse Ising model.
We prepared several spin configurations, which were obtained using repeated
observations of the model for a particular strength of the transverse field, as
input data for the neural network. Although the proposed method can be employed
using experimental observations of quantum many-body systems, we tested our
technique with spin configurations generated by a quantum Monte Carlo
simulation without initial relaxation. The neural network successfully
classified the strength of transverse field only from the spin configurations,
leading to consistent estimations of the critical point of our model $\Gamma_c
=J$.
