Christopher Baldwin
Michigan State University, MI·Physics
A huge number of practical problems — routing delivery trucks, balancing an investment portfolio, designing a drug molecule — boil down to finding the best compromise among competing constraints. Classical computers often choke on hard versions of these optimization problems, so one of the central questions in quantum computing is whether quantum machines can do better.
- Fields
Physics
Computer Science
- Topics
adiabatic quantum computation
quantum annealing
quantum optimization
- Grants
- 1 known grant, through May 2028
What they work on
- Test whether catalysts proposed to improve adiabatic quantum computation retain their advantage on spin-glass models, the statistical-physics analogues of hard optimization problems, and identify the mechanisms behind each success or failure.
- Calculate free energy landscapes — the terrain of barriers and valleys that makes optimization hard — in canonical spin-glass models, and quantify how different catalysts reshape them, using path integrals, mean-field decouplings, and the replica method.
- Characterize how robust catalysts remain under realistic hardware imperfections such as finite temperature and noise, separating features essential to their functioning from extraneous ones.
- Design improved catalysts that address the identified weaknesses of existing proposals, informing how to adapt them to different classes of optimization problems.
More about the research
The standard quantum strategy is adiabatic quantum computation (AQC), which encodes a problem so that its answer is the lowest-energy state of a physical system, then slowly turns down quantum fluctuations so the system settles into that state. Early results looked promising, but it is now understood that plain AQC tends to get stuck for much the same reasons classical algorithms do. Attention has shifted to "catalysts": deliberate modifications to the AQC procedure meant to route around those obstacles. This project uses statistical physics to work out what catalysts can and cannot do.
Why it matters
If quantum computers could reliably beat classical ones on hard optimization problems, the consequences would reach logistics, finance, and drug design. By explaining in physical terms why proposed catalysts for adiabatic quantum computation succeed or fail, and how noise and finite temperature degrade them, this work could guide which modifications are worth implementing on real hardware and inform the design of the next generation of quantum optimization algorithms.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Alex Kamenev
University of Minnesota, MN·Physics
Extend non-equilibrium quantum field theory to include Lindbladian dynamics, which describe driven qubit arrays that lose energy to their surroundings.
Aldo Romero
West Virginia University, WV·Materials Science
Extend the DMFTwDFT code into a unified open-source framework combining DFT, DFT+U and dynamical mean field theory on the VASP and SIESTA codes, supporting both collinear and noncollinear magnetic configurations.
Monika Filipovska
University of Connecticut, CT·Engineering
Develop quantum simulation algorithms that encode spatial and temporal dependencies of stochastic transportation networks into quantum states, using quantum amplitude estimation and dimensionality reduction to improve efficiency.
The school
Michigan State University: 28 labs on PI Pages, $932M a year on research, 555 research doctorates a year.
Reach out
A start for your email. Fill in the brackets.