PeopleofSuperintelligence

Researcher

Matthew P. Harrigan

Also known as Matthew Harrigan, Matt Harrigan

Matthew P. Harrigan is a quantum computing researcher whose work spans machine learning for protein dynamics and software for quantum algorithms. At Stanford, he studied protein dynamics under Vijay Pande and contributed to molecular-simulation and visualization software. He later coauthored research on quantum optimization and a paper presenting Qualtran, an open-source library for quantum algorithms. As of October 2026, he writes quantum software with Google Quantum AI and coauthors research on reinforcement learning for quantum error correction.

Now at
Google

Timeline

  1. 2017

    Landmark kernel tICA

    His PhD work at Stanford under Vijay Pande studies protein dynamics with computation and machine learning. In 2017, he introduces landmark kernel tICA, which uses a Nyström kernel approximation for nonlinear analysis of molecular dynamics.

  2. 2017

    OpenMM 7 and MSMExplorer

    At Stanford's Department of Chemistry, he contributes software to OpenMM 7, a molecular-dynamics toolkit. He also coauthors MSMExplorer, a paper on data visualizations for biomolecular dynamics.

  3. January 2019

    Rigetti at QIP 2019

    In January 2019, he joins QIP's industry session as Matt Harrigan of Rigetti.

  4. 2020

    Variational quantum optimizers

    In 2020, he coauthors a study on using models to improve optimizers for variational quantum algorithms. The work addresses classical optimization under sampling and gate errors on noisy quantum computers.

  5. 2020

    QAOA on Sycamore

    In 2020, he coauthors a study applying the quantum approximate optimization algorithm to graph problems on Google's Sycamore superconducting processor. The team tests instances up to 23 qubits.

  6. 2021

    Low-depth quantum optimization

    In 2021, he coauthors low-depth constructions for quantum optimization that connect quantum walks with classical continuous relaxations. The paper uses these connections to analyze how quantum dynamics shape optimization.

  7. 2024

    The Qualtran library

    In 2024, he coauthors Qualtran, an open-source library for representing and analyzing quantum algorithms. The library can simulate and test algorithms, generate diagrams, and tabulate resource needs.

  8. As of October 2026

    Quantum software at Google

    As of October 2026, he writes quantum software with Google Quantum AI. In March 2026, he coauthors an APS presentation on implementing quantum rotation synthesis in Qualtran. In July 2026, he coauthors a Nature study that uses reinforcement learning to control quantum error correction on Willow, improving surface-code logical stability 3.5-fold under injected drift.

Career

  1. NowGoogle
  2. 2019Rigetti

Education

  1. Stanford UniversityPhD

From the biography.

  1. Google had the most people shaping AI every year from 2014 to 2025Includes Google. Our estimates of which organizations employed the most people shaping AI each year from 1955 to 2025, tracing the shift from university labs to companies.
  2. Google has the highest combined h-index among AI organizationsIncludes Google. We estimate that Google holds the highest combined h-index at 8,016, while universities account for 73% of the deduplicated total across AI organizations.
  3. 20% of people at companies who shape AI also hold a university role, led by Google and Stanford UniversityIncludes Google. We estimate that 539 of the 6,766 people with a known current organization (8%) hold both a university role and a company role, including 20% of people at companies and 13% of people at universities.
  4. Google alumni have founded or led 133 other organizations, more than any other AI labIncludes Google. We estimate that alumni of 17 major AI labs went on to found or lead 350 other organizations, including 226 they founded, and we trace 472 career moves directly between 23 labs since 2010.

Sources and credits

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  1. Matthew Harrigan mpharrigan.com. Checked 21 October 2026.
  2. Landmark Kernel tICA for Conformational Dynamics mpharrigan.com. Checked 21 October 2026.
  3. OpenMM 7: Rapid development of high performance algorithms for molecular dynamics journals.plos.org. Checked 21 October 2026.
  4. MSMExplorer: Data Visualizations for Biomolecular Dynamics joss.theoj.org. Checked 21 October 2026.
  5. QIP 2019: Program details jila.colorado.edu. Checked 21 October 2026.
  6. Using Models to Improve Optimizers for Variational Quantum Algorithms research.google. Checked 21 October 2026.
  7. Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor arxiv.org. Checked 21 October 2026.
  8. Low-Depth Mechanisms for Quantum Optimization research.google. Checked 21 October 2026.
  9. Expressing and Analyzing Quantum Algorithms with Qualtran research.google. Checked 21 October 2026.
  10. Reinforcement learning control of quantum error correction nature.com. Checked 21 October 2026.
  11. Matthew P Harrigan research.google. Checked 21 October 2026.
  12. Practical Rotation Synthesis - APS Global Physics Summit 2026 meetings-archive.aps.org. Checked 21 October 2026.