RGTIW - Rigetti Computing Awarded Phase 2 of DARPA Benchmarking Program | Benzinga
BERKELEY, Calif., Nov. 21, 2023 (GLOBE NEWSWIRE) -- Rigetti Computing, Inc. (NASDAQ:RGTI) ("Rigetti" or the "Company"), a pioneer in full-stack quantum-classical computing, today announced that it was awarded Phase 2 of the Defense Advanced Research Projects Agency (DARPA) Quantum Benchmarking Program to develop benchmarks for quantum application performance on large-scale quantum computers. The award is worth up to $1.5 million based on the achievement of certain milestones.
The goal of the DARPA Benchmarking Program is to create key quantum computing metrics for fault tolerant quantum computing, make those metrics testable, and estimate the required quantum and classical resources needed to reach critical performance thresholds. The three-year project comprises two phases. Rigetti was awarded Phase 1 in March 2022, and Phase 2 will be completed in March 2025. The University of Technology Sydney, Aalto University, and the University of Southern California will continue to be project partners in Phase 2.
"We are very proud to have been recognized for the work we delivered in Phase 1. This is a testament to the entire team's performance. This work allows us to get a deeper understanding of what areas in our quantum system need improvement to get closer to fault tolerance, and how our quantum computers need to scale in order to solve some of humanity's most important and pressing problems," says Dr. Subodh Kulkari, CEO of Rigetti.
The key output of Phase 1 was the development of a resource estimation framework to provide insight into the requirements of a superconducting quantum computing system necessary for solving large-scale, complex problems. Phase 2 will entail refining and optimizing our estimates for selected utility-scale problems, delivering new upper bounds on these requirements.
Another benefit of this resource estimation framework is to enable a cost benefit analysis into whether the resources needed to run a quantum application will be met by the value of solving the particular problem. A challenge in developing quantum algorithms is understanding how a problem will scale, and at what point a dataset is large or complex enough to benefit from the unique properties ...