License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.TQC.2022.4
URN: urn:nbn:de:0030-drops-165114
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2022/16511/
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Flammia, Steven T.

Averaged Circuit Eigenvalue Sampling

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LIPIcs-TQC-2022-4.pdf (0.7 MB)

Abstract

We introduce ACES, a method for scalable noise metrology of quantum circuits that stands for Averaged Circuit Eigenvalue Sampling. It simultaneously estimates the individual error rates of all the gates in collections of quantum circuits, and can even account for space and time correlations between these gates. ACES strictly generalizes randomized benchmarking (RB), interleaved RB, simultaneous RB, and several other related techniques. However, ACES provides much more information and provably works under strictly weaker assumptions than these techniques. Finally, ACES is extremely scalable: we demonstrate with numerical simulations that it simultaneously and precisely estimates all the Pauli error rates on every gate and measurement in a 100 qubit quantum device using fewer than 20 relatively shallow Clifford circuits and an experimentally feasible number of samples. By learning the detailed gate errors for large quantum devices, ACES opens new possibilities for error mitigation, bespoke quantum error correcting codes and decoders, customized compilers, and more.

BibTeX - Entry

@InProceedings{flammia:LIPIcs.TQC.2022.4,
  author =	{Flammia, Steven T.},
  title =	{{Averaged Circuit Eigenvalue Sampling}},
  booktitle =	{17th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2022)},
  pages =	{4:1--4:10},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-237-2},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{232},
  editor =	{Le Gall, Fran\c{c}ois and Morimae, Tomoyuki},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2022/16511},
  URN =		{urn:nbn:de:0030-drops-165114},
  doi =		{10.4230/LIPIcs.TQC.2022.4},
  annote =	{Keywords: Quantum noise estimation, quantum benchmarking, QCVV}
}

Keywords: Quantum noise estimation, quantum benchmarking, QCVV
Collection: 17th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2022)
Issue Date: 2022
Date of publication: 04.07.2022
Supplementary Material: The source code used to perform the simulations and generate the figures is available on GitHub:
Software (Source Code): https://github.com/sflammia/ACES

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