The high-performance platform for hybrid quantum-classical computing

To do algorithm research and build applications for future quantum advantages, a bridging technology is needed to enable dynamic workflows across disparate system architectures. With a unified and open programming model, NVIDIA CUDA-Q is an open-source platform for integrating and programming quantum processing units (QPUs), GPUs, and CPUs in one system. CUDA-Q enables GPU-accelerated system scalability and performance across heterogeneous QPU, CPU, GPU, and emulated quantum system elements.

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NVIDIA CUDA-Q platform
NVIDIA CUDA-Q is built for hybrid application development by offering a unified programming model designed for a hybrid setting—that is, CPUs, GPUs, and QPUs working together. It consists of language extensions for Python and C++ and a system-level toolchain that enables application acceleration.

Key Benefits

NVIDIA CUDA-Q improves productivity and scalability in quantum algorithm research


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

NVIDIA CUDA-Q is an open platform

Flexible Platform

Connects to partner QPUs and GPU simulators, easy toolchain integration, and interoperates with modern GPU-accelerated applications.

 NVIDIA CUDA-Q is high performing

High Performing

2500X simulation speedup on a four A100 GPU for up to 26 qubits, and scaling to 40 qubits by distributing the simulation across 128 GPU nodes.

A Range of Features

  • Kernel-based programming model extending C++ and Python for hybrid quantum-classical systems
  • 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 simulators; partnering with QPU builders across different qubit types
  • Interoperable with CUDA and the CUDA software ecosystem

Built for Performance

NVIDIA CUDA-Q enables straightforward execution of hybrid code on many different types of quantum processors, simulated or physical. Researchers can leverage the cuQuantum-accelerated simulation backends as well as QPUs from our partners or connect their own simulator or quantum processor.

NVIDIA CUDA-Q can significantly speed up quantum algorithms, compared to other quantum frameworks. Quantum algorithms can achieve a speedup of up to 2500X over CPU, scaling number of qubits using multiple GPUs.

Variational quantum eigensolver running on both cuQuantum and Quantinuum’s H1 trapped ion QPU
Typical QML workflow in CUDA-Q using multi-threaded CPU versus multiple NVIDIA A100 Tensor Core GPUs.

Quantum Computing Partners

 Quantum Computing Partner - Alice & Bob
Quantum Computing Partner - Atom Computing
Quantum Computing Partner - IonQ
Quantum Computing Partner - IQM
Quantum Computing Partner - QuEra Computing
Quantum Computing Partner - Orca Computing
Quantum Computing Partner - Oxford Quantum Circuits
Quantum Computing Partner - Pasqal
Quantum Computing Partner - qBraid
Quantum Computing Partner - QCWare
Quantum Computing Partner - Quantinuum
Quantum Computing Partner - Quantum Brilliance
Quantum Computing Partner - Quantum Machines
Quantum Computing Partner - Rigetti
Quantum Computing Partner - Rigetti
Quantum Computing Partner - Terra Quantum
Quantum Computing Partner - Xanadu
Quantum Computing Partner - Zapata
Quantum Computing Partner - Zapata

Get started with CUDA-Q today.

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