License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.TQC.2013.106
URN: urn:nbn:de:0030-drops-43185
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2013/4318/
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Granade, Christopher E. ; Ferrie, Christopher ; Wiebe, Nathan ; Cory, D. G.

Robust Online Hamiltonian Learning

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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. 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. We further illustrate the practicality of our algorithm by applying it to two test problems: (1) learning an unknown frequency and the decoherence time for a single-qubit quantum system and (2) learning couplings in a many-qubit Ising model Hamiltonian with no external magnetic field.

BibTeX - Entry

@InProceedings{granade_et_al:LIPIcs:2013:4318,
  author =	{Christopher E. Granade and Christopher Ferrie and Nathan Wiebe and D. G. Cory},
  title =	{{Robust Online Hamiltonian Learning}},
  booktitle =	{8th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2013)},
  pages =	{106--125},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-55-2},
  ISSN =	{1868-8969},
  year =	{2013},
  volume =	{22},
  editor =	{Simone Severini and Fernando Brandao},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2013/4318},
  URN =		{urn:nbn:de:0030-drops-43185},
  doi =		{10.4230/LIPIcs.TQC.2013.106},
  annote =	{Keywords: Quantum information, sequential Monte Carlo, Bayesian, experiment design, parameter estimation}
}

Keywords: Quantum information, sequential Monte Carlo, Bayesian, experiment design, parameter estimation
Collection: 8th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2013)
Issue Date: 2013
Date of publication: 13.11.2013

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