Rigetti Computing, a pioneer in hybrid quantum-classical computing systems, has been awarded up to $8.6 million from the Defense Advanced Research Projects Agency (DARPA), as part of a larger collaboration with the NASA Quantum Artificial Intelligence Laboratory (QuAIL) and Universities Space Research Association (USRA), to develop a full-stack system with proven quantum advantage for solving real world problems.
In particular, the work will address complex scheduling problems that remain hard or impossible for classical computers to solve. Using quantum computers to find new solutions 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 optimization, or vehicle routing.
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“We’re honored to be chosen by DARPA and believe we are uniquely positioned to demonstrate quantum advantage for this class of problem,” said Mandy Birch, Senior Vice President, Engineering Strategy at Rigetti. “We believe strongly in an integrated hardware and software approach, which is why we’re bringing together the scalable Rigetti chip architecture with the algorithm design and optimization techniques pioneered by the NASA-USRA team.”
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The collaboration will focus on developing a superconducting quantum processor, hardware-aware software, and custom algorithms based on real-world scenarios. The work will leverage Rigetti’s Fab-1—the only dedicated quantum integrated circuit foundry in the U.S.—to manufacture chips that scale beyond 100 qubits. In addition, the NASA-USRA team will design methods for benchmarking the hardware against classical computers to determine quantum advantage.
The grant is part of the DARPA ONISQ (Optimization with Noisy Intermediate-Scale Quantum) program. The goal of the program is to establish that quantum information processing using NISQ devices has a quantitative advantage for solving real-world combinatorial optimization problems as compared with the best known classical methods.
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