Gregory Bentsen
William & Mary, VA·Physics
Quantum computers are meant to solve problems ordinary computers cannot, but today's machines are noisy and error-prone. That raises a practical problem: how do you tell whether a quantum processor is really doing something quantum, and really doing it correctly?
- Fields
Physics
Computer Science
Mathematics & Statistics
- Topics
quantum information science
randomized benchmarking
NISQ hardware
- Grants
- 1 known grant, through Aug 2029
What they work on
- Develop Brownian random quantum circuits, models whose random operations vary continuously in time, into a framework for precision randomized benchmarking that handles scrambling, decoherence, weak measurements, and faulty operations together.
- Design noisy Bell-sampling protocols that could certify quantum advantage using far fewer measurement samples than conventional tests on noisy hardware.
- Build noisy many-body echo and quantum-chaos probes that distinguish genuinely quantum dynamics from dynamics that an ordinary classical computer could simulate.
- Create trainable Brownian circuit ensembles that learn the detailed noise profile of a specific quantum processor and generate useful random circuit distributions for benchmarking.
More about the research
Some current performance tests can be imitated or convincingly reproduced by ordinary classical computers, which makes their results hard to trust. This project builds better diagnostic tools for testing and validating near-term quantum hardware, drawing on mathematical models of quantum chaos, the study of how quantum information spreads and scrambles across many interacting particles.
Why it matters
Trustworthy ways to certify what a quantum processor can actually do are a prerequisite for using these machines on real problems. Benchmarks that resist noise and cannot be faked by classical simulation would help set rigorous standards for validating quantum devices, which the grant links to longer-term applications in materials science, drug discovery, artificial intelligence, and secure information processing.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Akimasa Miyake
University of New Mexico, NM·Physics
Determine how symmetry-protected topological order, a collective entanglement found in frustrated quantum spin systems, serves as a resource for quantum computation within measurement-based quantum computation.
Milad Marvian
University of New Mexico, NM·Physics·Early-career
Develop mathematical models describing how errors propagate through quantum circuits, and use them to construct resource-efficient fault-tolerant computing subroutines.
Steven Girvin
Yale University, CT·Physics
Design and deploy superconducting quantum hardware in which each logical qubit is one microwave photon shared between two long-lived resonators. This 'dual-rail' encoding turns photon loss into a detectable, flagged erasure error.
The school
William & Mary: 11 labs on PI Pages, $95M a year on research, 67 research doctorates a year.
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