Intelligent Systems: Universities Space Research Association to Lead a DARPA Project on Quantum Computing in Collaboration with NASA QuAIL
March 26, 2020
On March 26, 2020 Universities Space Research Association (USRA) announced that DARPA has awarded the organization and its partners Rigetti Computing and the NASA Quantum Artificial Intelligence Laboratory (QuAIL) to work as a team to advance the state of art in quantum optimization. USRA, as the prime contractor of the award, will manage the collaboration.
The collaboration will focus on developing a superconducting quantum processor, hardware aware software and custom algorithms that take direct advantage of the hardware advances to solve scheduling and asset allocation problems. In addition, the team will design methods for benchmarking the hardware against classical computers to determine quantum advantage.
In particular, the work will target scheduling problems whose complexity goes beyond what has been done so far with the quantum approximate optimization algorithm (QAOA). USRA’s Research Institute for Advanced Computer Science (RIACS) has been working on quantum algorithms for planning and scheduling for NASA QuAIL since 2012. “The innovations on quantum gates performed by Rigetti coupled perfectly with the recent research ideas at QuAIL, enabling an unprecedented hardware-theory co-design opportunity” explains Dr. Davide Venturelli, USRA Associate Director for Quantum Computing and project PI for USRA. Understanding how to use quantum computers for scheduling applications could have important implications for national security such as real time strategic asset deployment, as well as commercial applications including global supply chain management, network optimizations or vehicle routing.
The grant is a part of the DARPA Optimization with Noisy Intermediate-Scale Quantum program (ONISQ). The goal of this program is to establish that quantum information processing using NISQ devices has a quantitative advantage for solving real-world-combinatorial optimization problems using the QAOA method.
Story contributed by USRA Newsroom (Suraiya Farukhi, Ph.D.)
See the USRA newsroom for more information.
