Rolls-Royce plans to use the new circuit on its journey to quantum advantage in CFD for modeling the performance of jet engine designs in simulations that use both classical and quantum computing methods.
Applying both classical and quantum computing methods directly to the challenge of designing jet engines will help us accelerate our processes and perform more sophisticated calculations.
—Leigh Lapworth, computational science fellow at Rolls-Royce
Rolls-Royce and its partner, Israel-based Classiq, designed the circuit using Classiq’s synthesis engine and then simulated it using NVIDIA A100 Tensor Core GPUs. The speed and scale of the process was made possible by NVIDIA cuQuantum, a software development kit that includes optimized libraries and tools to speed up quantum computing workflows.
NVIDIA offers a unified computing platform for quantum research and development across disciplines. The NVIDIA Grace Hopper Superchip, which combines the performance of NVIDIA Hopper architecture GPUs with the versatility of the NVIDIA Grace CPUs, is designed for giant-scale quantum simulation workloads.
Additionally, its high-speed, low-latency NVIDIA NVLink-C2C interconnect makes classical systems built with the superchip optimally suited to link to quantum processors, or QPUs. With a total 600GB of fast-accessible memory per node, Grace Hopper enables the quantum ecosystem to push these simulations to an even larger scale.
A strategic bridge to the quantum future, Grace Hopper powers DGX Quantum, the first GPU-accelerated quantum computing system combining quantum computing with state-of-the-art classical computing. NVIDIA also provides developers with NVIDIA CUDA Quantum, a robust open-source programming model that links GPUs and QPUs.
The Jülich Supercomputer Centre, one of Europe’s largest facilities for quantum computing, also announced plans to build a quantum computing lab with NVIDIA, highlighting the growing importance of hybrid quantum-classical computing systems. The lab will also help developers advance the field of quantum computing with tools such as CUDA Quantum.
Additionally, ORCA Computing is the latest QPU builder to integrate CUDA Quantum, combining its photonic quantum computer with GPUs for machine learning. TensorFlow Quantum and TorchQuantum—two popular quantum machine learning frameworks—now also integrate cuQuantum. The majority of the world’s quantum computing software today supports GPU acceleration with the NVIDIA quantum platform.
