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.2019.7
URN: urn:nbn:de:0030-drops-103995
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2019/10399/
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Gamble, John ; Granade, Christopher ; Wiebe, Nathan

Bayesian ACRONYM Tuning

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LIPIcs-TQC-2019-7.pdf (0.6 MB)

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.

BibTeX - Entry

@InProceedings{gamble_et_al:LIPIcs:2019:10399,
  author =	{John Gamble and Christopher Granade and Nathan Wiebe},
  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 =	{Wim van Dam and Laura Mancinska},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2019/10399},
  URN =		{urn:nbn:de:0030-drops-103995},
  doi =		{10.4230/LIPIcs.TQC.2019.7},
  annote =	{Keywords: Quantum Computing, Randomized Benchmarking}
}

Keywords: Quantum Computing, Randomized Benchmarking
Collection: 14th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2019)
Issue Date: 2019
Date of publication: 31.05.2019

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