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: https://drops.dagstuhl.de/opus/volltexte/2013/4318/
Granade, Christopher E. ; Ferrie, Christopher ; Wiebe, Nathan ; Cory, D. G.
Robust Online Hamiltonian Learning
| pdf-format: |
|
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 | |
| Seminar: | 8th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2013) | |
| Issue date: | 2013 | |
| Date of publication: | 13.11.2013 |
