• Corpus ID: 118674637

Inferring the quantum density matrix with machine learning

@article{Cranmer2019InferringTQ,
  title={Inferring the quantum density matrix with machine learning},
  author={Kyle Cranmer and Siavash Golkar and Duccio Pappadopulo},
  journal={arXiv: Quantum Physics},
  year={2019}
}
We introduce two methods for estimating the density matrix for a quantum system: Quantum Maximum Likelihood and Quantum Variational Inference. In these methods, we construct a variational family to model the density matrix of a mixed quantum state. We also introduce quantum flows, the quantum analog of normalizing flows, which can be used to increase the expressivity of this variational family. The eigenstates and eigenvalues of interest are then derived by optimizing an appropriate loss… 

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