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Title: Gate Set Tomography

Abstract

Gate set tomography (GST) is a protocol for detailed, predictive characterization of logic operations (gates) on quantum computing processors. Early versions of GST emerged around 2012-13, and since then it has been refined, demonstrated, and used in a large number of experiments. This paper presents the foundations of GST in comprehensive detail. The most important feature of GST, compared to older state and process tomography protocols, is that it is calibration-free . GST does not rely on pre-calibrated state preparations and measurements. Instead, it characterizes all the operations in a gate set simultaneously and self-consistently, relative to each other. Long sequence GST can estimate gates with very high precision and efficiency, achieving Heisenberg scaling in regimes of practical interest. In this paper, we cover GST's intellectual history, the techniques and experiments used to achieve its intended purpose, data analysis, gauge freedom and fixing, error bars, and the interpretation of gauge-fixed estimates of gate sets. Our focus is fundamental mathematical aspects of GST, rather than implementation details, but we touch on some of the foundational algorithmic tricks used in the pyGSTi implementation.

Authors:
 [1];  [2];  [1];  [3];  [1];  [1]
+ Show Author Affiliations
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Microsoft Research, Redmond, WA (United States)
  3. IBM, Yorktown Heights, NY (United States). Thomas J. Watson Research Center
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1828793
Report Number(s):
SAND-2021-10706J
Journal ID: ISSN 2521-327X; 699056
Grant/Contract Number:  
NA0003525
Resource Type:
Accepted Manuscript
Journal Name:
Quantum
Additional Journal Information:
Journal Volume: 5; Journal ID: ISSN 2521-327X
Publisher:
Quantum Science Open Community
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Nielsen, Erik, Gamble, John King, Rudinger, Kenneth, Scholten, Travis, Young, Kevin, and Blume-Kohout, Robin. Gate Set Tomography. United States: N. p., 2021. Web. doi:10.22331/q-2021-10-05-557.
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Nielsen, Erik, Gamble, John King, Rudinger, Kenneth, Scholten, Travis, Young, Kevin, & Blume-Kohout, Robin. Gate Set Tomography. United States. https://doi.org/10.22331/q-2021-10-05-557
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Nielsen, Erik, Gamble, John King, Rudinger, Kenneth, Scholten, Travis, Young, Kevin, and Blume-Kohout, Robin. Tue . "Gate Set Tomography". United States. https://doi.org/10.22331/q-2021-10-05-557. https://www.osti.gov/servlets/purl/1828793.
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@article{osti_1828793,
title = {Gate Set Tomography},
author = {Nielsen, Erik and Gamble, John King and Rudinger, Kenneth and Scholten, Travis and Young, Kevin and Blume-Kohout, Robin},
abstractNote = {Gate set tomography (GST) is a protocol for detailed, predictive characterization of logic operations (gates) on quantum computing processors. Early versions of GST emerged around 2012-13, and since then it has been refined, demonstrated, and used in a large number of experiments. This paper presents the foundations of GST in comprehensive detail. The most important feature of GST, compared to older state and process tomography protocols, is that it is calibration-free. GST does not rely on pre-calibrated state preparations and measurements. Instead, it characterizes all the operations in a gate set simultaneously and self-consistently, relative to each other. Long sequence GST can estimate gates with very high precision and efficiency, achieving Heisenberg scaling in regimes of practical interest. In this paper, we cover GST's intellectual history, the techniques and experiments used to achieve its intended purpose, data analysis, gauge freedom and fixing, error bars, and the interpretation of gauge-fixed estimates of gate sets. Our focus is fundamental mathematical aspects of GST, rather than implementation details, but we touch on some of the foundational algorithmic tricks used in the pyGSTi implementation.},
doi = {10.22331/q-2021-10-05-557},
journal = {Quantum},
number = ,
volume = 5,
place = {United States},
year = {Tue Oct 05 00:00:00 EDT 2021},
month = {Tue Oct 05 00:00:00 EDT 2021}
}
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https://doi.org/10.22331/q-2021-10-05-557
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