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 Communications, Physical 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.

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.

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:

• T. Lubinski et al., Application-Oriented Performance Benchmarks for Quantum Computing. IEEE Transactions on Quantum Engineering 4, 1-32 (April 2023).
• T. Proctor et al., Scalable randomized benchmarking of quantum computers using mirror circuits. Physical Review Letters 129, 150502 (October 2022).
• R. Blume-Kohout et al., A taxonomy of small Markovian errors. PRX Quantum 3, 020335 (May 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).

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:

• A DOE Science Highlight, “Probing the Inner Workings of High-Fidelity Quantum Processors,” showcased our recent collaborative research with UNSW (Sydney), and the growing impact of gate set tomography.
• A Nature News & Views editorial, "Silicon qubits move a step closer to achieving error correction" highlighted our 2022 Nature cover article "Precision tomography of a three-qubit electron-nuclear quantum processor in silicon". This result’s significance, and the role played by gate set tomography, was explained in a YouTube video and news releases by Sandia and ScienceInPublic.
• IEEE Spectrum published "New Standards Rolling Out for Clocking Quantum-Computer Performance" covering our 2022 Nature Physics paper "Measuring the performance of quantum computers". The news was also picked up by HPCWire, NewsBreak, Hardware-Specs, and Science Daily.
• An IonQ blog post, "Benchmarking our next-generation system" highlighted Timothy Proctor’s 2021 collaborative paper "Application-Oriented Performance Benchmarks for Quantum Computing" with the Quantum Economic Development Consortium (QED-C).
• Quantum commissioned a Perspective, "Gate set tomography is not just hyperaccurate, it’s a different way of thinking", covering our 2021 article "Gate set tomography".
• Quantum commissioned a Perspective, "Crosstalk diagnosis for the next generation of quantum processors", highlighting our 2020 article "Detecting crosstalk errors in quantum information processors".

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

• May, 2023: Welcome to Yale Fan, who has joined the QPL as a postdoctoral fellow.
• April, 2023: IEEE Transactions on Quantum Engineering has published "Application-Oriented Performance Benchmarks for Quantum Computing", by Tim Proctor and a cadre of QED-C researchers, about a pioneering suite of application-centric benchmarks.
• March, 2023: Welcome to Andrew Guo, who has joined the QPL as a postdoctoral fellow.
• February, 2023: Welcome to Ashe Miller, who has joined the QPL as a postdoctoral fellow.
• December, 2022: Congratulations to Corey Ostrove, who is now a QPL staff scientist.
• October, 2022: Physical Review Letters has published "Scalable randomized benchmarking of quantum computers using mirror circuits," which introduces and demonstrates the first form of randomized benchmarking that can scale to hundreds of qubits.
• July, 2022: The QPL is excited to announce that Timothy Proctor has received a prestigious DOE Early Career award for proposed research in “Quantum Capability Learning”. Dr. Proctor and the QPL plan to develop scalable proxy programs for efficiently testing quantum computers, and machine learning and multi-scaling modeling methods that predict a quantum computer’s computational power from proxy program data.