Nvidia launches quantum computing platform

Nvidia, the darling of high performance computing (HPC), is bringing new attention to quantum computing.

The company has launched its Nvidia Quantum Optimized Device Architecture, or QODA. This hybrid platform is designed to make quantum computing more accessible by enabling programming of quantum and classical applications in a single, consolidated environment. According to Nvidia, it aims to accelerate breakthroughs in quantum research and development in AI, HPC, healthcare, finance and other disciplines.

The goal is to make QODA for quantum computing just like CUDA is for GPU computing – an industry standard. (CUDA is C-like code for writing specialized HPC and AI applications that run on Nvidia GPUs.)

Nvidia said HPC and AI developers can use QODA to add quantum computing to existing applications, leveraging both quantum processors as well as future simulated quantum machines using Nvidia DGX systems and Nvidia GPUs available in centers. scientific computing and public clouds.

This isn’t Nvidia’s first dance into the quantum computing space. About a year ago, the company released cuQuantum, a software development kit (SDK) for accelerating quantum workflows using Tensor Cores in its GPUs along with various libraries and tools optimized for tasks such as circuit simulations. quantum.

In announcing the new architecture, Nvidia announced collaborations with a host of quantum computing companies, most with a Q in their name: IQM hardware vendors Quantum Computers, Pasqal, Quantinuum, Quantum Brilliance, and Xanadu; software providers QC Ware and Zapata Computing; and the supercomputing centers Forschungszentrum Jülich, Lawrence Berkeley National Laboratory and Oak Ridge National Laboratory, which is interesting because ORNL is an all-AMD store.

What is Quantum Computing?

Processes have evolved, but the basic structure of computing has not changed since its invention. Data is represented in its most basic state as bits, 0s or 1s. Quantum computing uses what is called a quibit, which can represent a 0, a 1, or any proportion of 0s and 1s superimposed on the two states. A quibit can be 1/4 0 and 3/4 1, for example.

This means several things. First, much greater speed. Quantum computing can process data up to 1,000 times faster than standard binary computers. You are not going to use a quantum computer to run Microsoft Excel. You will use a quantum computer to do weather simulations and drug tests.

Second, quantum computing has no compatibility with current applications. You’re not just going to rewrite or recompile an application on a quantum computer. You have to rewrite everything from scratch to take full advantage of the new architecture. No one will find that appealing, and that’s what Nvidia is trying to fix.

Fortunately, this is not one situation or the other. Applications that will be accelerated by quantum processing units (QPUs) will be hybrid workloads that leverage a standard supercomputing architecture for large parts of an application, while the most critical parts are accelerated by a quantum system.

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