Robust Online Hamiltonian Learning

Chris Granade1,2, Chris Ferrie1,3, Nathan Wiebe1,4 and D. G. Cory1,5,6

2012 New Journal of Physics 14 103013  
arXiv:1207.1655 doi:10.1088/1367-2630/14/10/103013

Presented as a poster at TQC 2013 (Poster PDF, Proceedings). Presented as a contributed talk at DAMOP 2013 (Slides PDF).

Abstract

In this work we combine two distinct machine learning methodologies, sequential Monte Carlo and Bayesian experimental design, and apply them to the problem of inferring the dynamical parameters of a quantum system. We design the algorithm with practicality in mind by including parameters that control trade-offs between the requirements on computational and experimental resources. The algorithm can be implemented online (during experimental data collection), avoiding the need for storage and post-processing. Most importantly, our algorithm is capable of learning Hamiltonian parameters even when the parameters change from experiment-to-experiment, and also when additional noise processes are present and unknown. The algorithm also numerically estimates the Cramer-Rao lower bound, certifying its own performance.

Software Resources and Demonstrations

QInfer, a Python-language implementation of the algorithm presented in this work, is available from GitHub.

[source code for above demo]

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Affiliations

  1. Institute for Quantum Computing, University of Waterloo.
  2. Department of Physics, University of Waterloo.
  3. Department of Applied Mathematics, University of Waterloo.
  4. Department of Combinatorics and Optimization, University of Waterloo.
  5. Department of Chemistry, University of Waterloo.
  6. Perimeter Institute for Theoretical Physics.