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Analyzing massive datasets from nuclear physics experiments can take hours or days to process, but researchers are working to radically reduce that time to mere seconds using special software being developed at the Department of Energy’s Lawrence Berkeley and Oak Ridge national laboratories.

Working in collaboration with researchers from Oak Ridge National Laboratory, D-Wave Quantum Inc., a quantum computing systems, software and services provider, has shown its annealing quantum computing prototype has the potential to operate faster than the leading supercomputing systems.

The Department of Energy’s Quantum Computing User Program, or QCUP, is releasing a Request for Information to gather input from all relevant parties on the current and upcoming availability of quantum computing resources, conventions for measuring, tracking, and forecasting quantum computing performance, and methods for engaging with the diversity of stakeholders in the quantum computing community. Responses received to the RFI will inform QCUP on both immediate and near-term availability of hardware, software tools and user engagement opportunities in the field of quantum computing.

The Exascale Small Modular Reactor effort, or ExaSMR, is a software stack developed over seven years under the Department of Energy’s Exascale Computing Project to produce the highest-resolution simulations of nuclear reactor systems to date. Now, ExaSMR has been nominated for a 2023 Gordon Bell Prize by the Association for Computing Machinery and is one of six finalists for the annual award, which honors outstanding achievements in high-performance computing from a variety of scientific domains.

A force within the supercomputing community, Jack Dongarra developed software packages that became standard in the industry, allowing high-performance computers to become increasingly more powerful in recent decades.

In the early 2000s, high-performance computing experts repurposed GPUs — common video game console components used to speed up image rendering and other time-consuming tasks