Nvidia Unveils QODA Platform for Quantum Classic Computing

Today, at the Q2B conference in Tokyo, GPU and AI kingpin Nvidia announces QODA — the Quantum Optimized Device Architecture designed to create a single programming environment for hybrid classical-quantum computing. Similar in general purpose (and name) to Nvidia’s CUDA (Compute Unified Device Architecture) parallel computing development platform, QODA takes the highly specialized quantum development discipline and makes it accessible to a wider range of software developers. But the storyline for Nvidia GPUs in the quantum world is more nuanced than even in AI, and the aim of QODA is to make it straightforward.

Brave quantum world

“It’s a very different world than ten years ago” Timothy Costa, Nvidia’s director of HPC and Quantum Computing Products told VentureBeat. Costa explained what’s behind the progress the quantum industry has made: “What we’re seeing is the industry is moving from systems with one or two qubits, most of them in academia, to today, to systems with more than 200 qubits in size. the cloud. †

Qubits are the rough equivalent of bits in classical computing, but while they can be read since they have a value of zero or one, qubits can have multiple values ​​at once, making them and the hardware that creates them the essence of quantum computing.

QODA welcomes all developers on board

QODA’s credo is to help non-quantum-specialized developers take advantage of these advances in the industry. It is particularly aimed at developers targeting certain domains, including drug discovery, chemistry, finance, and optimization (as a general computational engineering), where quantum can speed things up and make it possible to tackle problems that would otherwise be computationally impractical. would be to tackle. These areas benefit best from a combination of classical computing (albeit in the powerful form of HPC – high performance computing) and quantum.

Nvidia’s GPU technology is of course already a dominant platform in the HPC world. But it also turns out to be specifically applicable on the quantum side. That’s because, while GPUs are not quantum hardware, they can serve as a more effective medium for quantum circuit emulation than CPUs, as GPUs can implement state vector and tensor network methods, which speed up quantum circuit simulations. That means a big GPU system like Nvidia’s DGX platformmay be particularly good at handling hybrid scenarios, as it provides a single physical infrastructure layer that can serve both classical and quantum computing workloads.

QODA capitalizes on this new GPU “split personality” potential by offering a single platform for hybrid development. Underlying this is from Nvidia cuQuantum SDK and its DGX Quantum Appliance. The cuQuantum SDK allows developers to simulate quantum circuits on GPUs. It includes integration with quantum computing frameworks Approx† kiss kit and Pennylane† The DGX Quantum Appliance is a software container that integrates the frameworks with cuQuantum and runs on all Nvidia hardware.

With these underlying technologies, QODA offers two things to make quantum computing more accessible to conventional developers:

• A kernel-based programming model for developing quantum computers with interfaces to commonly used programming languages, such as C++ and Python,
• A compiler suitable for quantum and classical compute instructions came together in the same source code, as shown in the image below.

Example of hybrid coding with block of quantum code at the top and GPU-oriented code below.
Credit: Nvidia

To combine virtual and physical

QODA and cuQuantum work with emulated QPUs (quantum processor units) on GPU hardware, but they also work with physical QPUs, so code written on the platform is transferable between emulated and physical environments. In fact, QODA and cuQuatum have been co-developed with numerous vendors in the quantum space, including hardware partners such as IQM quantum computers† Pascal† quantum† Quantum brilliance and Xanadu† software/algorithm partners such as QC goods and Zapata computers† and supercomputing centers, including: Julich Research Center† NERSC/Lawrence Berkeley National Laboratoryand Oak Ridge National Laboratory† The diversity of hardware partners involved means that QODA also works with a variety of qubit modalities, including superconducting, neutral atoms, trapped ions, diamond processors and photonics.

What’s in store for Nvidia and Quantum?

Costa told VentureBeat that with QODA, Nvidia hopes to give developers access to disruptive computing technology and enable domain scientists to leverage quantum acceleration, closely linked to the best of GPU supercomputing.

Nvidia sees it as QODA’s mission to enable developers focused on a class of applications (rather than quantum computing itself) to use quantum and see it as a technology that can accelerate what they’re already doing. This is a pragmatic approach to the adoption of quantum computing, which could be the biggest change in computing since the introduction of the microcomputer — or maybe even the mainframe.

Nvidia’s goal with its partner strategy with QODA is to bring many startups together, with the likely effect of fostering cohesion and an ecosystem in the quantum arena. This is essential for maturing the space and making it more attractive for adoption by corporate clients. Just as Nvidia has helped make AI and autonomous cars useful to major customers, the QODA announcement should help make quantum computing more industrialized and commercially viable.