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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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.

Explore key benefits.


Flexible and Scalable

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


Open Platform

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



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

Offering a range of features.

  • 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

Quantum computing partners.

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