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.
@article{arxiv.2103.16781,
title = {Bidirectional information flow quantum state tomography},
author = {Huikang Huang and Haozhen Situ and Shenggen Zheng},
journal= {arXiv preprint arXiv:2103.16781},
year = {2021}
}