Yuanyuan Yang
Stony Brook University, NY·Computer Science
Computing systems keep hitting limits in scale and speed, and two very different kinds are studied here. Quantum computers promise to solve problems far beyond today's machines, but a single quantum processor holds only a few thousand qubits (quantum bits), while key uses need tens of thousands to millions.
- Fields
Computer Science
Engineering
- Topics
quantum computing
qubits
quantum processing unit
- Grants
- 2 known grants, through Sep 2028
What they work on
- 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.
- Identify optimal physical interconnections between QPUs for virtual quantum machines and build a simulator to evaluate these designs.
- Develop a hardware-enhanced architecture that speeds data processing, transmission and distribution for serverless functions running on edge devices.
- Design data-aware computation orchestration and computation-aware data orchestration systems that adapt to edge conditions, and evaluate the framework through simulations and a prototype.
More about the research
Meanwhile, edge computing runs software on small devices near users, such as Internet-of-Things gadgets, but these devices are limited and varied. Both projects ask how to design systems that link many limited resources into one efficient, scalable whole.
Why it matters
Scalable quantum clusters could make room for applications needing many qubits, such as quantum machine learning, chemistry and drug development. Faster serverless edge computing could improve data-heavy Internet-of-Things services that need low delay and strong privacy, making edge applications easier and cheaper to build.
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.
Somali Chaterji
Purdue University, IN·Computer Science·Early-career
Develop on-device machine learning analytics for farm sensors that adapt to each device's computing power, changing network conditions and competition from co-located applications.
Shaahin Angizi
New Jersey Institute of Technology, NJ·Engineering·Early-career
Design reconfigurable circuits and architectures for a multi-modal in-sensor accelerator that moves AI computation onto sensor nodes as efficiently as possible.
The school
Stony Brook University: 32 labs on PI Pages, $319M a year on research, 289 research doctorates a year.
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