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Bayesian inference for quantum state tomography

Author

Listed:
  • D. S. Gonçalves
  • C. L. N. Azevedo
  • C. Lavor
  • M. A. Gomes-Ruggiero

Abstract

We present a Bayesian approach to the problem of estimating density matrices in quantum state tomography. A general framework is presented based on a suitable mathematical formulation, where a study of the convergence of the Monte Carlo Markov Chain algorithm is given, including a comparison with other estimation methods, such as maximum likelihood estimation and linear inversion. This analysis indicates that our approach not only recovers the underlying parameters quite properly, but also produces physically acceptable punctual and interval estimates. A prior sensitive study was conducted indicating that when useful prior information is available and incorporated, more accurate results are obtained. This general framework, which is based on a reparameterization of the model, allows an easier choice of the prior and proposal distributions for the Metropolis–Hastings algorithm.

Suggested Citation

  • D. S. Gonçalves & C. L. N. Azevedo & C. Lavor & M. A. Gomes-Ruggiero, 2018. "Bayesian inference for quantum state tomography," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(10), pages 1846-1871, July.
    • Handle: RePEc:taf:japsta:v:45:y:2018:i:10:p:1846-1871
    DOI: 10.1080/02664763.2017.1401049
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    Cited by:

    1. The Tien Mai, 2023. "An efficient adaptive MCMC algorithm for Pseudo-Bayesian quantum tomography," Computational Statistics, Springer, vol. 38(2), pages 827-843, June.

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