Efficient quantum state tomography with convolutional neural networks

@article{Schmale2021EfficientQS,
  title={Efficient quantum state tomography with convolutional neural networks},
  author={Tobias Schmale and Moritz Reh and Martin G{\"a}rttner},
  journal={npj Quantum Information},
  year={2021},
  volume={8},
  pages={1-8}
}
Modern day quantum simulators can prepare a wide variety of quantum states but the accurate estimation of observables from tomographic measurement data often poses a challenge. We tackle this problem by developing a quantum state tomography scheme which relies on approximating the probability distribution over the outcomes of an informationally complete measurement in a variational manifold represented by a convolutional neural network. We show an excellent representability of prototypical… 
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Optimizing Design Choices for Neural Quantum States

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Efficient quantum state tomography with mode-assisted training

Apply mode-assisted training that provides global information via the modes of the NN distribution to quantum state tomography using restricted Boltzmann machines and improves the quality of reconstructed quantum states by orders of magnitude.

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