  The exact reconstruction of many-body quantum systems is one of the major
challenges in modern physics, because it is impractical to overcome the
exponential complexity problem brought by high-dimensional quantum many-body
systems. Recently, machine learning techniques are well used to promote quantum
information research and quantum state tomography has been also developed by
neural network generative models. We propose a quantum state tomography method,
which is based on Bidirectional Gated Recurrent Unit neural network (BiGRU), to
learn and reconstruct both easy quantum states and hard quantum states in this
paper. We are able to use fewer measurement samples in our method to
reconstruct these quantum states and obtain high fidelity.
