Gate Set Tomography

Nielsen, Erik; Gamble, John King; Rudinger, Kenneth; Scholten, Travis; Young, Kevin; Blume-Kohout, Robin

doi:10.22331/q-2021-10-05-557

Gate Set Tomography

Journal Article · 05 October 2021 · Quantum

DOI:https://doi.org/10.22331/q-2021-10-05-557· OSTI ID:1828793

Nielsen, Erik ^[1]; Gamble, John King ^[2]; Rudinger, Kenneth ^[1]; Scholten, Travis ^[3]; Young, Kevin ^[1]; Blume-Kohout, Robin ^[1]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Microsoft Research, Redmond, WA (United States)
IBM, Yorktown Heights, NY (United States). Thomas J. Watson Research Center

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.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: NA0003525

OSTI ID:: 1828793

Report Number(s):: SAND-2021-10706J; 699056

Journal Information:: Quantum, Vol. 5; ISSN 2521-327X

Publisher:: Quantum Science Open CommunityCopyright Statement

Country of Publication:: United States

Language:: English

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