Quantum Computing Inc. Enters 3-Year Agreement with Los Alamos National Laboratory in Exascale & Petascale Simulations

(GlobeNewsWire) Quantum Computing Inc. a leader in bridging the power of classical and quantum computing, today announced a three-year cooperative research and development agreement (CRADA) with Los Alamos National Laboratory (Los Alamos), a U.S. Department of Energy (DOE) multidisciplinary research institution and renowned computing pioneer. QCI will collaborate with Los Alamos scientists through its administrator, Triad National Security, LLC, on a key component of large-scale simulations that are critical for a range of high-value, real-world applications, including national security.
The cooperation is focused on deriving specifications for solving next-generation graph partitioning problems and evaluating current and future graph-partitioning algorithms implemented in QCI’s Qatalyst™ quantum software, enabling more efficient petascale (1015 floating point operations per second or petaFLOPS) and, eventually exascale (1018 FLOPS), simulations.
These kinds of simulations are decomposed to run on a computational grid employing millions of processors in configurations where avoidance of unnecessary communication of data is essential to achieving high sustained performance. QCI’s technology focuses on the decomposition and partitioning of graphs that represent the supercomputing grid, preventing computational load imbalances, while assuring the desired performance and results.
QCI’s Qatalyst quantum software will be used to process Los Alamos’ computational meshes via a focus on hybrid classical/quantum algorithms. Achieving petascale and especially exascale simulations requires larger meshes that can benefit from QCI’s recently announced QGraph™ graph-analytic capabilities. The collaboration will exercise quantum processor units (QPUs) in concert with classical processors to partition large graphs more optimally, after automatically converting graph partitioning problems to the more general constrained-optimization form. The hybrid work will initially be done with D-Wave annealing-based QPUs, and eventually include gate-model QPUs via algorithms such as the Quantum Approximate Optimization Algorithm (QAOA).
“It is a privilege and great opportunity to work with an esteemed institution and computing trailblazer such as Los Alamos,” said Robert Liscouski, CEO of QCI. “We are honored to be a trusted advisor and partner in solving problems that are of such critical importance.”