NVIDIA QODA

The Platform for Hybrid Quantum-Classical Computing

For the programmer who wants to do algorithm research and build hybrid applications for future quantum advantage, a bridging technology is needed to enable dynamic workflows across disparate system architectures. With a unified programming model, NVIDIA Quantum-Optimized Device Architecture (QODA) is a first-of-its-kind platform for hybrid quantum-classical computers, enabling integration and programming of quantum processing units (QPUs), GPUs, and CPUs in one system. QODA enables GPU-accelerated system scalability and performance across heterogeneous QPU, CPU, GPU, and emulated quantum system elements.

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Image alt text — QODA consists of both a specification and a compiler NVQ++. It delivers a unified programming model designed for quantum processors (either actual or emulated) in a hybrid setting—that is, CPUs, GPUs, and QPUs working together.

Key Benefits of QODA

Flexible and Scalable

Supports hybrid deployments via emulation on a single GPU up to NVIDIA DGX SuperPOD™ and with multiple QPU partner backends

Open

Connects to any type of QPU backend, allowing accessibility to all users

High Performing

287X speedup in end-to-end Variational Quantum Eigensolver (VQE) performance with 20 qubits and dramatically improved scaling compared to Pythonic frameworks

Easily Integrated

Interoperates with modern GPU-accelerated applications

Productive

Streamlines hybrid quantum-classical development with a unified environment, improving productivity and scalability in quantum algorithm research

Features of QODA

Kernel-based programming model extending C++ for hybrid quantum-classical systems (full Python support on the way)
Native support for GPU hybrid compute, enabling GPU pre- and post-processing and classical optimizations
System-level compiler toolchain featuring split compilation with NVQ++ compiler for quantum kernels, lowering to Multi-Level Intermediate Representation (MLIR) and Quantum Intermediate Representation (QIR)

Initial NVQ++ benchmark shows 287X improvement in end-to-end VQE performance with 20 qubits and dramatically improved scaling with system size compared to standard Pythonic implementation
Standard library of quantum algorithmic primitives
Interoperable with partner QPUs as well as simulated QPUs using the cuQuantum GPU platform; partnering with QPU builders across many different qubit types