  Along the way initiated by Carleo and Troyer [1], we construct the
neural-network quantum state of transverse-field Ising model(TFIM) by an
unsupervised machine learning method. Such a wave function is a map from the
spin-configuration space to the complex number field determined by an array of
network parameters. To get the ground state of the system, values of the
network parameters are calculated by a Stochastic Reconfiguration(SR) method.
We provide for this SR method an understanding from action principle and
information geometry aspects. With this quantum state, we calculate key
observables of the system, the energy, correlation function, correlation
length, magnetic moment and susceptibility. As innovations, we provide a high
efficiency method and use it to calculate entanglement entropy (EE) of the
system and get results consistent with previous work very well.
