Nvidia has introduced Quantum Optimized Device Architecture (QODA), a platform for hybrid quantum-classical computing that is intended to make quantum computing more accessible.
Introduced July 12, QODA provides a coherent hybrid quantum-classical programming model, Nvidia said. The platform enables integration and programming of quantum processing units (QPUs), GPUs, and CPUs in one system, allowing HPC and AI experts to add quantum computing to existing applications.
QODA applications can leverage current quantum processors, simulated future quantum machines using Nvidia DGX systems, and Nvidia GPUs. A unified, kernel-based programming model extends C++ and Python for hybrid quantum-classical systems. Other QODA features include:
- Support for any kind of QPU, physical or emulated.
- A compiler for hybrid systems.
- A standard library of quantum primitives.
- Interoperability with current applications.
Developers can apply as early interest participants in QODA through the Nvidia developer site.
Nvidia believes that all valuable quantum applications will be hybrid, in which a quantum computer will work alongside a high-performance classical computer. These applications will leverage GPU-accelerated supercomputing, supplemented or accelerated by quantum. Applications that will benefit from quantum include those in areas such as drug discovery, chemistry, finance, and energy.
QODA will support quantum processors from companies such as IQM, Pasqual, Quantinuum, Quantum Brilliance, and Xanadu. Quantum software companies such as Qcware and Zapata are collaborating with Nvidia as well. Supercomputing centers are working with Nvidia to test and deploy QODA for thousands of scientific computing developers around the world.
In an emulated environment, QODA leverages Nvidia’s cuQuantum technology, an SDK of libraries and tools for accelerating quantum workflows. Developers can use the SDK and Nvidia GPU Tensor Core GPUs to speed up quantum circuit simulations.