Ten miles in from Long Island's Atlantic coast, Shinjae Yoo is revving his engine. The computational scientist and machine learning group lead at the U.S. Department of Energy's Brookhaven National Laboratory is one of many researchers gearing up to run quantum computing simulations on a supercomputer for the first time, thanks to new software.

Yoo's engine, the Perlmutter supercomputer at the National Energy Research Scientific Computing Center (NERSC), is using the latest version of PennyLane, a quantum programming framework from Toronto-based Xanadu. The open-source software, which builds on the NVIDIA cuQuantum software development kit, lets simulations run on high-performance clusters of NVIDIA GPUs. The performance is key because researchers like Yoo need to process ocean-size datasets. He'll run his programs across as many as 256 NVIDIA A100 Tensor Core GPUs on Perlmutter to simulate about three dozen qubits—the powerful calculators quantum computers use. That's about twice the number of qubits most researchers can model these days.

People agree: accelerated computing is energy-efficient computing. The National Energy Research Scientific Computing Center (NERSC), the U.S. Department of Energy's lead facility for open science, measured results across four of its key high performance computing and AI applications.

They clocked how fast the applications ran and how much energy they consumed on CPU-only and GPU-accelerated nodes on Perlmutter, one of the world's largest supercomputers using NVIDIA GPUs. The results were clear. Accelerated with NVIDIA A100 Tensor Core GPUs, energy efficiency rose 5x on average. An application for weather forecasting logged gains of 9.8x.