RQS Senior Investigator Joins DOE Initiative to Advance AI for Quantum Computing
July 31, 2026
The U.S. Department of Energy's Genesis Mission is a new $250 million initiative designed to accelerate AI-driven scientific discovery.
A senior investigator in the NSF Quantum Leap Challenge Institute for Robust Quantum Simulation (RQS) is part of a research project selected for the U.S. Department of Energy's (DOE) Genesis Mission, a new initiative designed to accelerate AI-driven scientific discovery.
Xiaodi Wu, an associate professor of computer science with an appointment in the University of Maryland Institute for Advanced Computer Studies (UMIACS), is part of a Virginia Tech-led team that will use AI to design more efficient quantum error-correcting codes, a key challenge in building practical quantum computers.
Today's quantum systems require extensive resources to reliably protect even a single unit of quantum information, and discovering improved error-correcting codes is computationally prohibitive using conventional methods. By leveraging AI to accelerate the design process, the team aims to develop tools that can help the broader quantum computing community identify more effective codes faster. The project received $720,000 in Phase I funding.
Announced July 22 at the inaugural Genesis Mission Summit in Washington, D.C., the DOE selected 278 projects nationwide to receive a combined $250 million in Phase I funding. Awardees will gain access to the Genesis Mission Platform, which includes AI agent frameworks, advanced AI models and software from industry partners, and high-performance computing resources at DOE national laboratories and partner facilities. Teams selected for Phase II will scale their initial prototypes into tools with broader real-world impact.
A second Genesis Mission project also includes UMIACS researchers Chris Metzler, Abhinav Bhatele, Maria Molina, and Haizhao Yang. Led by Jinwoong Yoo, an assistant research scientist at the University of Maryland and NASA Goddard Space Flight Center, the team is developing an agentic AI framework to overcome computational bottlenecks in the DOE's long-term climate forecasting system. The project seeks to improve localized, long-range predictions of floods and droughts to help protect critical infrastructure and received $750,000 in Phase I funding.
