QVis: A Visual Analytics Tool for Exploring Noise and Errors in Quantum Computing Systems

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present the preliminary design and results of QVis, a visual analytics tool for exploring quantum device performance data. QV is helps uncover temporal and multivariate variations in noise properties of quantum devices. We describe the implementations of these methods as well as applications to the analysis of a 127-qubit data set derived from the IBM washington processor over a 16-month period. Both human-interactive and semi-automated analytic methods are included to address requirements for visual exploration, thresholding, and clustering techniques. Our application of QVis to real-world scenarios demonstrates the ability to reveal noteworthy patterns in the behavior of the critical characterization metrics.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE International Conference on Quantum Computing and Engineering, QCE 2023
EditorsHausi Muller, Yuri Alexev, Andrea Delgado, Greg Byrd
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages211-214
Number of pages4
ISBN (Electronic)9798350343236
DOIs
StatePublished - 2023
Event4th IEEE International Conference on Quantum Computing and Engineering, QCE 2023 - Bellevue, United States
Duration: Sep 17 2023Sep 22 2023

Publication series

NameProceedings - 2023 IEEE International Conference on Quantum Computing and Engineering, QCE 2023
Volume2

Conference

Conference4th IEEE International Conference on Quantum Computing and Engineering, QCE 2023
Country/TerritoryUnited States
CityBellevue
Period09/17/2309/22/23

Keywords

  • clustering
  • quantum computing
  • system reliability
  • temporal analysis
  • visual analytics

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