NVIDIA is teaming up with Google Quantum AI to accelerate the design of next-generation quantum computing devices using simulations powered by NVIDIA’s CUDA-Q platform. This collaboration aims to overcome current limitations of quantum computing hardware, which can only run a certain number of quantum operations before computations must cease due to “noise.”
According to Guifre Vidal, research scientist from Google Quantum AI, scaling up quantum hardware while keeping noise in check is crucial for developing commercially useful quantum computers. To achieve this, NVIDIA’s CUDA-Q platform and Eos supercomputer are being used to simulate the physics of Google’s quantum processors. This enables complex dynamical simulations that fully capture how qubits within a quantum processor interact with their environment.
With 1,024 NVIDIA H100 Tensor Core GPUs, Google can perform one of the world’s largest and fastest dynamical simulations of quantum devices at a fraction of the cost. As Tim Costa, director of quantum and HPC at NVIDIA, notes, AI supercomputing power will be essential to quantum computing’s success.
Accelerating Quantum AI Processor Design with Simulation of Quantum Device Physics
NVIDIA has announced its collaboration with Google Quantum AI to accelerate the design of next-generation quantum computing devices using simulations powered by the NVIDIA CUDA-Q platform. This partnership aims to overcome the current limitations of quantum computing hardware, which can only run a certain number of quantum operations before computations must cease due to “noise.” The development of commercially useful quantum computers relies on scaling up quantum hardware while keeping noise in check.
To achieve this, Google Quantum AI is utilizing the hybrid quantum-classical computing platform and the NVIDIA Eos supercomputer to simulate the physics of its quantum processors. This involves complex dynamical simulations capable of fully capturing how qubits within a quantum processor interact with their environment. Traditionally, these simulations have been prohibitively computationally expensive to pursue. However, by employing 1,024 NVIDIA H100 Tensor Core GPUs at the NVIDIA Eos supercomputer, Google can perform one of the world’s largest and fastest dynamical simulations of quantum devices — at a fraction of the cost.
The CUDA-Q platform enables fully comprehensive, realistic simulations of devices containing 40 qubits — the largest-performed simulations of this kind. The simulation techniques provided by CUDA-Q mean noisy simulations that would have taken a week can now run in minutes. This accelerated capability will be publicly available in the CUDA-Q platform, allowing quantum hardware engineers to rapidly scale their system designs.
Overcoming Noise Limitations in Quantum Computing
One of the primary challenges in developing commercially useful quantum computers is overcoming noise limitations in quantum computing hardware. Currently, these devices can only run a certain number of quantum operations before computations must cease due to “noise.” This noise arises from the interactions between qubits within a quantum processor and their environment. To overcome this limitation, researchers need to understand and mitigate the effects of noise on quantum hardware designs.
Google Quantum AI is using NVIDIA accelerated computing to explore the noise implications of increasingly larger quantum chip designs. By simulating the physics of its quantum processors, Google can identify and address potential noise issues early in the design process. This approach enables the development of more robust and reliable quantum computing devices that can perform complex calculations with greater accuracy.
The Role of AI Supercomputing Power in Quantum Computing
The success of quantum computing relies on the integration of artificial intelligence (AI) supercomputing power. NVIDIA’s CUDA-Q platform demonstrates the central role GPU-accelerated simulations have in advancing quantum computing to help solve real-world problems. By leveraging AI supercomputing power, researchers can perform complex dynamical simulations that were previously prohibitively computationally expensive.
The collaboration between Google Quantum AI and NVIDIA highlights the importance of AI supercomputing power in accelerating the design of next-generation quantum computing devices. This partnership showcases the potential of GPU-accelerated simulations to overcome current limitations in quantum computing hardware and pave the way for the development of commercially useful quantum computers.
The Future of Quantum Computing: Rapid Scaling and Real-World Applications
The accelerated capability provided by the CUDA-Q platform will be publicly available, allowing quantum hardware engineers to rapidly scale their system designs. This will enable the development of more powerful and reliable quantum computing devices that can perform complex calculations with greater accuracy.
As quantum computing continues to advance, it is expected to have a significant impact on various industries, including chemistry, materials science, and optimization problems. The ability to simulate complex systems and processes will lead to breakthroughs in fields such as medicine, energy, and climate modeling. The collaboration between Google Quantum AI and NVIDIA marks an important step towards realizing the potential of quantum computing to solve real-world problems.
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