Nvidia and Google Quantum AI are working together to improve the design of upcoming quantum computing devices. Google Quantum AI uses a mixed quantum-classical computing platform with the Nvidia Eos supercomputer to model the physics of its quantum processors. This helps address the limitations of quantum computing hardware, such as “noise” that can hinder its operations. By using Nvidia’s accelerated computing, Google aims to investigate the effects of noise on larger quantum chip designs. They are able to do this with the CUDA-Q platform and 1,024 Nvidia H100 Tensor Core GPUs on the Nvidia Eos supercomputer, allowing for fast and cost-effective simulations.
According to Tim Costa, director of quantum and HPC at Nvidia, AI supercomputing capability will play a critical role in the success of quantum computing. The use of the CUDA-Q platform emphasizes the importance of GPU-accelerated simulations in advancing quantum computing. With the help of CUDA-Q and H100 GPUs, Google can now run detailed and realistic simulations of devices with 40 qubits, which was previously not possible. The software for these dynamic simulations will be publicly available through the CUDA-Q platform, enabling quantum hardware engineers to quickly scale their system designs.
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