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Quantum Performance Laboratory

The Quantum Performance Laboratory (QPL) is a research and development (R&D) group within Sandia National Laboratories.  We develop and deploy cutting-edge techniques for assessing the performance of quantum computing hardware, serving the needs of the U.S. government, industry, and academia.  

What we do

The QPL studies the performance of quantum computing devices, and develops practical methods to assess it. Our research produces:

  • insight into the failure mechanisms of real-world quantum computing processors,
  • well-motivated metrics of low- and high-level performance,
  • predictive models of multi-qubit quantum processors, and
  • concrete, tested protocols for evaluating as-built experimental processors.

We develop, maintain, and support the open-source pyGSTi software package. PyGSTi provides an extensive suite of tools and algorithms for evaluating individual qubits and many-qubit processors.   We collaborate with a wide range of partners in industry and academia to develop new performance assessment tools and apply them to newly developed quantum computing platforms. We publish our research results in scientific journals including Nature, Nature Physics, Nature CommunicationsPhysical Review X, PRX Quantum, and Physical Review Letters.  See our Publications page for details.

In addition to our R&D capabilities, the QPL also provides quantum hardware assessment capabilities directly to the Department of Energy (DOE) and the U.S. Government.

Who we are

QPL researchers include Sandia research staff, postdoctoral fellows, PhD students from several research universities, and affiliates. As of 2022, our core personnel include six permanent staff scientists, three postdoctoral researchers, and three PhD students. For more details, see our People page.

Our research

The Quantum Performance Lab is, first and foremost, a research organization. Our goal is to extend the frontiers of understanding performance of quantum computers and quantum computing components — e.g. qubits, gates, logical components and subroutines, and fully integrated quantum computing systems.

We pursue this goal through mathematical theory, numerical analysis, creation of new algorithms and software, and experimental tests and demonstrations in real-world quantum computing systems. We publish our research in journals and conferences, but we also implement (and test) our research in the pyGSTi open-source software.

To learn more about our research — what we study, and what we’ve discovered and created — see our Research Products page.


We share our research results with the broader quantum computing community through collaboration, open-source software, and — most importantly — peer-reviewed publications. QPL researchers have co-authored more than 30 papers since 2014. For a complete list, see our Publications page. Here are some recent highlights:

  • A. Hashim et al., Benchmarking verified logic operations for fault tolerance. arXiv preprint:2207.08786 (July 2022).
  • J. Hines et al., Demonstrating scalable randomized benchmarking of universal gate sets. arXiv preprint:2207.07272 (July 2022).
  • T. Proctor et al., Establishing trust in quantum computations. arXiv preprint:2204.07568 (April 2022).
  • R. Blume-Kohout et al.A taxonomy of small Markovian errors. PRX Quantum 3, 020335 (2022)
  • A. R. Mills et al.Two-qubit silicon quantum processor with operation fidelity exceeding 99%.  Science Advances 8, abn5130 (April 2022).
  • M. Mądzik et al.Precision tomography of a three-qubit electron-nuclear quantum processor in silicon. Nature 601, 348–353 (January 2022).
  • K. Rudinger et al., Characterizing mid-circuit measurements on a superconducting qubit using gate set tomography, Phys. Rev. Applied 17, 014014 (January 2022).
  • T. Proctor et al., Measuring the Capabilities of Quantum Computers. Nature Physics 18, 75-79 (January, 2022).
  • E. Nielsen et al., Gate set tomographyQuantum 5, 557 (October 2021).
  • R. Blume-Kohout and K. YoungA volumetric framework for quantum computer benchmarks, Quantum 4, 362 (November 2020).
  • T. Proctor et al., Detecting and tracking drift in quantum information processors, Nature Communications 11, 5396 (October 2020).

QPL research and accomplishments have been highlighted by media, social media, and scientific editorials. If you are a journalist interested in the QPL’s work, please contact Robin Blume-Kohout (QPL co-lead) or Troy Rummler (Sandia Corporate Communications). Some examples of media coverage of the QPL and our accomplishments include:


Here’s the latest news from the QPL! See our complete list of Announcements for more.