Guide users through installing, configuring, and using CUDA-Q in Python and C++.
npx skills add https://github.com/NVIDIA/skills --skill cudaq-guide
Please help me install this Agent Skill. Skill: cudaq-guide Source: https://github.com/NVIDIA/skills/tree/main/skills/CUDA-Q/cudaq-guide Command: npx skills add https://github.com/NVIDIA/skills --skill cudaq-guide
CUDA-Q onboarding and usage guide that helps developers install CUDA-Q, write quantum kernels, run simulations, and access QPU hardware with built-in applications and tutorials.
Begin with the CUDA-Q Getting Started Guide to install, test, and begin building quantum applications.
High-intent search queries and answers about installing and using this skill.
Install CUDA-Q by following the step-by-step getting started guide for Python and C++ environments. This validates your setup and allows you to immediately begin writing quantum kernels.
CUDA-Q is used to write quantum kernels, run GPU simulations, and access QPU hardware. It provides a unified environment for quantum kernel development across research and development scenarios.
Yes, CUDA-Q supports running GPU simulations and provides access to QPU hardware. You can execute these workflows using built-in applications and parallelization examples.
Yes, CUDA-Q provides built-in applications, tutorials, and notebooks for quantum kernel development. These resources cover kernel simulation and hardware teardowns to help validate installations.
Validate your CUDA-Q installation by using the getting started guide to run built-in applications and parallelization examples. This confirms your Python and C++ environments are correctly configured.
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