Gushu Li
University of Pennsylvania, PA·Computer Science·Early-career
Quantum computers could change physics, chemistry, materials science and optimization. But the hardware is advancing faster than the software needed to use it well, and getting a quantum device into a working state is still largely done by hand.
- Fields
Computer Science
Engineering
Physics
- Topics
quantum computing
quantum compiler
compiler optimization
- Grants
- 2 known grants, through Sep 2029
What they work on
- Develop high-level intermediate representations and compilation algorithms that optimize quantum programs across large numbers of qubits and operations, rather than only making small local changes.
- Extend quantum compiler support beyond qubit-based machines to continuous-variable quantum computing and analog quantum simulators.
- Build testing and verification tools that check whether large-scale quantum program optimizations remain correct and scale to bigger programs.
- Simulate fully untuned silicon quantum dot devices by combining finite element electrostatic modeling with a constant-interaction quantum dot model, and build an FPGA-based emulator that returns charge-sensor signals in real time.
- Design autotuning algorithms for each stage of quantum dot tune-up from cold start, and validate them on quadruple quantum dot devices fabricated and measured in the lab.
More about the research
The lab works on both ends of this gap. One project redesigns the compiler, the software that translates a quantum program into instructions the hardware can run, so it can optimize programs more broadly. The other builds simulation tools and algorithms that automatically tune silicon quantum dot devices, a promising type of quantum hardware.
Why it matters
Better compilers could let today's limited quantum hardware run larger, more reliable programs, and could open up non-qubit platforms for chemistry, materials and optimization problems. Automated tuning, together with low-cost simulated test benches and training data, could cut the manual effort of preparing silicon quantum dot devices. This would make it easier to scale them up and give researchers without fabrication facilities a way to work on the problem.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Sonia Lopez Alarcon
Rochester Institute of Technology, NY·Computer Science
Train machine learning models to identify and synthesize qubit routing algorithms tailored to a specific quantum device and objective, including selection of optimal parameters and optimization functions.
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.
Yuanyuan Yang
Stony Brook University, NY·Computer Science
Develop qubit entanglement and mapping algorithms that let tens to hundreds of interconnected quantum processing units act as one virtual quantum machine with many more qubits.
The school
University of Pennsylvania: 90 labs on PI Pages, $2.2B a year on research, 750 research doctorates a year.
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