cudaq-guide

Gunakan untuk panduan penyiapan CUDA-Q, target simulasi, akses QPU, dan penulisan @cudaq.kernel.

npx skills add https://github.com/nvidia/cuda-quantum --skill cudaq-guide

CUDA-Q Guide

Purpose

Guide users through CUDA-Q installation, basic kernels, GPU simulation targets, QPU access, built-in applications, multi-GPU execution, and Python @cudaq.kernel authoring. For Qiskit-to-CUDA-Q ports, route to the qiskit-to-cudaq skill instead.

Prerequisites

Python 3.10+ for Python CUDA-Q workflows.
CUDA Toolkit and an NVIDIA GPU for GPU-accelerated targets on Linux.
CPU-only simulation is available through qpp-cpu; macOS is CPU-only.
C++ workflows require Linux or WSL and C++20.
QPU workflows require provider-specific credentials and accounts.

Instructions

Invoke with /cudaq-guide [argument].
If no argument is given, display the onboarding menu and ask which topic the user wants.
Use the routing table below to choose the relevant reference file.
Read local CUDA-Q documentation files when the answer depends on a specific CUDA-Q version or backend behavior.
Do not answer Qiskit porting questions from this skill; use qiskit-to-cudaq.

Routing by Argument

Argument	Action	Reference
`install`	Walk through Python or C++ installation and validation.	references/onboarding.md
`test-program`	Build and run a Bell-state kernel.	references/onboarding.md
`gpu-sim`	Select GPU, multi-GPU, tensor-network, or CPU targets.	references/onboarding.md
`qpu`	Guide provider selection and credential-safe QPU setup.	references/onboarding.md
`applications`	Summarize CUDA-Q application areas and notebooks.	references/onboarding.md
`parallelize`	Choose `mgpu`, `mqpu`, async dispatch, or distributed observe.	references/onboarding.md
`author`	Author CUDA-Q Python kernels, select execution APIs, and debug compiler issues.	references/authoring.md
(none)	Print the menu below and ask which topic to explore.	This file

CUDA-Q Getting Started

CUDA-Q is NVIDIA's unified quantum-classical programming model for CPUs, GPUs, and QPUs.
Supports Python and C++. Docs: https://nvidia.github.io/cuda-quantum/latest/

Choose a topic:
  /cudaq-guide install         Install CUDA-Q
  /cudaq-guide test-program    Write and run a Bell-state kernel
  /cudaq-guide gpu-sim         Accelerate simulation on NVIDIA GPUs
  /cudaq-guide qpu             Connect to real QPU hardware
  /cudaq-guide applications    Explore what you can build
  /cudaq-guide parallelize     Run across GPUs or QPUs
  /cudaq-guide author          Author @cudaq.kernel Python code

Reference Files

references/onboarding.md: installation, test program, GPU targets, QPU providers, application areas, parallelization modes, examples, and platform troubleshooting.
references/authoring.md: execution APIs, kernel-language constraints, silent-failure pitfalls, recurring coding patterns, resource metrics, debugging, and validation.

Limitations

Guidance targets CUDA-Q Python/C++ workflows, with authoring details focused on decorator-mode Python APIs used in CUDA-Q 0.14 and 0.15.
GPU and multi-GPU support depends on local CUDA-Q, CUDA Toolkit, driver, MPI, and hardware availability.
QPU access and target options are provider-specific and may change; verify against local docs before giving operational steps.

Troubleshooting

Import error after pip install cudaq: check Python 3.10+ and supported OS.
No GPU detected: verify CUDA Toolkit and nvidia-smi; fall back to qpp-cpu.
Kernel compile error: read references/authoring.md and check the restricted kernel-language subset.
Version-specific behavior differs: compare cudaq.__version__ with the latest documentation, then review relevant documentation or source changes when debugging an installed version that is not the latest release.
QPU submission fails: verify provider credentials are set as environment variables or through a secrets manager, never hardcoded.
Documentation lookup fails: retry transient MCP or repository lookup once, then fall back to local docs or official CUDA-Q documentation.