NVIDIA CUDA-Q
NVIDIA CUDA-Q? is the QPU-agnostic platform for accelerated quantum supercomputing.
Product Overview
CUDA-Q is an open-source quantum development platform orchestrating the hardware and software needed to run useful, large-scale quantum computing applications. The platform’s hybrid programming model allows computation on GPU, CPU, and QPU resources in tandem from within a single quantum program. CUDA-Q is “qubit-agnosticâ€â€”seamlessly integrating with all QPUs and qubit modalities and offering GPU-accelerated simulations when adequate quantum hardware is not available.
CUDA-Q extends simulation tools far beyond the NISQ-era—charting a course to large-scale, error-corrected quantum supercomputing.
Benefits
Productive
Streamlines hybrid quantum-classical development with a unified programming model, improving productivity and scalability in quantum algorithm research.
Future Proof
Hybrid application development at scale for future accelerated quantum supercomputers.
QPU agnostic and efficiently integrated with all qubit modalities.
High Performance
Up to 2500X speedups for large-scale simulations on GPUs. Best-in-class compiler and runtime tools that optimize user code with backend-specific tailored performance.
Open Platform
Interoperability with AI and HPC workflows and integration with quantum tools across the stack.
Quantum Hardware Design
Capabilities to design and simulate quantum systems on the path for accelerated quantum supercomputers.
AI for Quantum
Accelerate high-performance AI workflows that enable quantum applications.
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.
GPU Advantage
CUDA-Q quantum algorithms can achieve a speedup of up to 2500X over CPU, scaling the number of qubits using multiple GPUs.
Multiple GPU Scaling
Multiple GPUs can scale a quantum algorithm beyond today’s quantum devices.
Algorithm Speedup
NVIDIA CUDA-Q can significantly speed up quantum algorithms, compared to other quantum frameworks. Researchers from Chung Yuan Christian University in Taiwan were able to dramatically improve training and inference time using CUDA-Q over other quantum frameworks.
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Latest CUDA-Q News
Partner Stories
165+
qubits
4%
reduction in false positives
2%
increase in true positives
CUDA-Q enables simulation of 165+ qubit quantum computation for increasing fraud detection accuracy.
400k
hours of GPU access unlocked.
38+
world-class projects facilitated.
CUDA-Q grows NERSC’s quantum ecosystem, enabling researchers to study hybrid applications, quantum algorithms, and error correction in a supercomputing environment.
2.7X
training speedup over other GPU quantum simulators.
3.5X
less error compared to other GPU quantum simulators.
CUDA-Q accelerated a hybrid quantum neural network for predicting solar panel placement.
200X
speedup over CPU using NVIDIA DGX? Cloud.
6X
larger simulation with CUDA-Q than possible with existing HPC.
CUDA-Q was used to develop hybrid algorithms that draw on both quantum and classical resources to chemically model toxin-removing catalysts.
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