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DOI: 10.4230/LIPIcs.TQC.2019.7

Bayesian ACRONYM Tuning

Authors: John Gamble, Christopher Granade, and Nathan Wiebe

Published in: LIPIcs, Volume 135, 14th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2019)

Abstract

We provide an algorithm that uses Bayesian randomized benchmarking in concert with a local optimizer, such as SPSA, to find a set of controls that optimizes that average gate fidelity. We call this method Bayesian ACRONYM tuning as a reference to the analogous ACRONYM tuning algorithm. Bayesian ACRONYM distinguishes itself in its ability to retain prior information from experiments that use nearby control parameters; whereas traditional ACRONYM tuning does not use such information and can require many more measurements as a result. We prove that such information reuse is possible under the relatively weak assumption that the true model parameters are Lipschitz-continuous functions of the control parameters. We also perform numerical experiments that demonstrate that over-rotation errors in single qubit gates can be automatically tuned from 88% to 99.95% average gate fidelity using less than 1kB of data and fewer than 20 steps of the optimizer.

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John Gamble, Christopher Granade, and Nathan Wiebe. Bayesian ACRONYM Tuning. In 14th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 135, pp. 7:1-7:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@InProceedings{gamble_et_al:LIPIcs.TQC.2019.7,
  author =	{Gamble, John and Granade, Christopher and Wiebe, Nathan},
  title =	{{Bayesian ACRONYM Tuning}},
  booktitle =	{14th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2019)},
  pages =	{7:1--7:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-112-2},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{135},
  editor =	{van Dam, Wim and Man\v{c}inska, Laura},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TQC.2019.7},
  URN =		{urn:nbn:de:0030-drops-103995},
  doi =		{10.4230/LIPIcs.TQC.2019.7},
  annote =	{Keywords: Quantum Computing, Randomized Benchmarking}
}

Document

DOI: 10.4230/LIPIcs.TQC.2013.106

Robust Online Hamiltonian Learning

Authors: Christopher E. Granade, Christopher Ferrie, Nathan Wiebe, and D. G. Cory

Published in: LIPIcs, Volume 22, 8th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2013)

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.

Cite as

Christopher E. Granade, Christopher Ferrie, Nathan Wiebe, and D. G. Cory. Robust Online Hamiltonian Learning. In 8th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2013). Leibniz International Proceedings in Informatics (LIPIcs), Volume 22, pp. 106-125, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

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@InProceedings{granade_et_al:LIPIcs.TQC.2013.106,
  author =	{Granade, Christopher E. and Ferrie, Christopher and Wiebe, Nathan and Cory, D. G.},
  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 =	{Severini, Simone and Brandao, Fernando},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TQC.2013.106},
  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}
}

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Documents authored by Wiebe, Nathan

Bayesian ACRONYM Tuning

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Robust Online Hamiltonian Learning

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