  Quantum state tomography is a key process in most quantum experiments. In
this work, we employ quantum machine learning for state tomography. Given an
unknown quantum state, it can be learned by maximizing the fidelity between the
output of a variational quantum circuit and this state. The number of
parameters of the variational quantum circuit grows linearly with the number of
qubits and the circuit depth, so that only polynomial measurements are
required, even for highly-entangled states. After that, a subsequent classical
circuit simulator is used to transform the information of the target quantum
state from the variational quantum circuit into a familiar format. We
demonstrate our method by performing numerical simulations for the tomography
of the ground state of a one-dimensional quantum spin chain, using a
variational quantum circuit simulator. Our method is suitable for near-term
quantum computing platforms, and could be used for relatively large-scale
quantum state tomography for experimentally relevant quantum states.
