Nengkun Yu
Stony Brook University, NY·Computer Science
Quantum computers and sensors promise powerful new ways to process information. But learning what state a quantum system is in is much harder than reading out a classical device.
- Fields
Computer Science
Physics
Mathematics & Statistics
- Topics
quantum information
quantum computing
Pauli measurements
- Grants
- 1 known grant, through Jun 2030
What they work on
- Determine the fundamental limits on how much information can be learned about a quantum system using Pauli measurements, a practical, widely used type of quantum measurement.
- Develop efficient algorithms for quantum state tomography, which reconstructs a quantum state from measurement data, using only Pauli measurements.
- Design methods to verify quantum entanglement, the linked behavior of quantum particles, using Pauli measurements under realistic experimental conditions.
- Combine tools from information theory, statistics and computer science to make quantum learning methods reliable and efficient on current hardware.
More about the research
Some measurements extract a great deal of information yet need complex, costly operations. Others are easy to do on today's hardware but are less well understood. This project asks how much can be learned using the practical kind.
Why it matters
Better methods for checking quantum devices with simple, practical measurements could help engineers building quantum computers and other quantum technologies confirm that their hardware works as intended, narrowing the gap between quantum theory and scalable, reliable devices.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Yizhi You
Northeastern University, MA·Physics·Early-career
Construct microscopic models and hybrid fracton field theories to describe the collective behavior of fractons, particle-like excitations with restricted mobility, and the phases and phase transitions they form.
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.
Leonard Schulman
California Institute of Technology, CA·Computer Science
Relax the statistical separation assumption needed when a hidden global confounder takes a limited number of values, aiming instead for model identification measured in Wasserstein distance, a way of comparing probability distributions.
The school
Stony Brook University: 32 labs on PI Pages, $319M a year on research, 289 research doctorates a year.
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