A practical and efficient approach for Bayesian quantum state estimation
Preprint Posting Date2020-02-24
Permanent link to this recordhttp://hdl.handle.net/10754/666030
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AbstractBayesian inference is a powerful paradigm for quantum state tomography, treating uncertainty in meaningful and informative ways. Yet the numerical challenges associated with sampling from complex probability distributions hampers Bayesian tomography in practical settings. In this article, we introduce an improved, self-contained approach for Bayesian quantum state estimation. Leveraging advances in machine learning and statistics, our formulation relies on highly efficient preconditioned Crank-Nicolson sampling and a pseudo-likelihood. We theoretically analyze the computational cost, and provide explicit examples of inference for both actual and simulated datasets, illustrating improved performance with respect to existing approaches.
CitationLukens, J. M., Law, K. J. H., Jasra, A., & Lougovski, P. (2020). A practical and efficient approach for Bayesian quantum state estimation. New Journal of Physics, 22(6), 063038. doi:10.1088/1367-2630/ab8efa
SponsorsWe thank R S Bennink and B P Williams for discussions. This work was funded by the U.S. Department of Energy, Office of Advanced Scientific Computing Research, through the Quantum Algorithm Teams and Early Career Research Programs. This work was performed in part at Oak Ridge National Laboratory, operated by UT-Battelle for the U.S. Department of Energy under contract no. DE-AC05-00OR22725.
JournalNew Journal of Physics
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