  Solving ground states of quantum many-body systems has been a long-standing
problem in condensed matter physics. Here, we propose a new unsupervised
machine learning algorithm to find the ground state of a general quantum
many-body system utilizing the benefits of artificial neural network. Without
assuming the specific forms of the eigenvectors, this algorithm can find the
eigenvectors in an unbiased way with well controlled accuracy. As examples, we
apply this algorithm to 1D Ising and Heisenberg models, where the results match
very well with exact diagonalization.
