A practical and efficient approach for Bayesian quantum state estimation

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 introdu...

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Bibliographic Details
Published in:New journal of physics 2020-06, Vol.22 (6), p.63038
Main Authors: Lukens, Joseph M, Law, Kody J H, Jasra, Ajay, Lougovski, Pavel
Format: Article
Language:English
Subjects:
Bayesian analysis
Bayesian estimation
CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS
Computer simulation
Cost analysis
Machine learning
Physics
quantum tomography
Sampling
sampling methods
State estimation
Statistical inference
Tomography
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Summary: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.
ISSN:1367-2630
1367-2630
DOI:10.1088/1367-2630/ab8efa