A practical and efficient approach for Bayesian quantum state estimation

Joseph Lukens, Kody Law, Ajay Jasra, Pavel Lougovski

Research output: Contribution to journalArticlepeer-review


Bayesian 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.
Original languageEnglish
JournalNew Journal of Physics
Publication statusPublished - 30 Apr 2020


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