Fei Li
George Mason University, VA·Computer Science
New sequencing technologies can read out the activity of thousands of genes in each individual cell, producing huge and complex datasets that are reshaping how scientists understand tissues, diseases and drug responses. Current computer methods often struggle to analyze this single-cell omics data accurately and quickly.
- Fields
Computer Science
Biology
Mathematics & Statistics
- Topics
quantum computing
quantum algorithms
quantum annealing
- Grants
- 1 known grant, through Aug 2027
What they work on
- Develop quantum algorithms for clustering cells and comparing them across biological conditions by recasting these tasks as quadratic unconstrained binary optimization (QUBO) problems solved with quantum annealing on D-Wave machines.
- Model how cells respond over time to drug combinations using gate-based quantum algorithms on IonQ platforms and hybrid classical-quantum approaches.
- Apply the quantum algorithms to single-cell transcriptomic data from the mouse brain and to drug-response studies in multiple myeloma and ovarian cancer to test their advantages.
- Build QOTBox, an open, scalable quantum computing platform for single-cell data analysis, with online tutorials and interactive notebooks.
More about the research
This project asks whether quantum computing, which uses the rules of quantum mechanics to solve certain problems more efficiently than ordinary computers, can do better.
Why it matters
Faster, more accurate analysis of single-cell data could speed the study of how cells behave and respond to treatment, including drug combinations in multiple myeloma and ovarian cancer, and could support more explainable drug discovery. Open software and teaching materials could help train scientists working across biology, computer science and quantum technology.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
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.
Dmitri Babikov
Marquette University, WI·Chemistry
Develop the Quantum Annealer Eigensolver (QAE), an algorithm for D-Wave quantum annealers, to calculate rotational-vibrational spectra of molecules and describe chemical reactivity.
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.
The school
George Mason University: 15 labs on PI Pages, $282M a year on research, 297 research doctorates a year.
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