# cudaq-guide

Walks you through CUDA-Q installation, simulation targets, QPU access and Python kernel authoring. Useful when you need to get moving with NVIDIA's quantum stack.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 2500 installs · ★ 3735
- Author: CUDA-Q Team
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/NVIDIA/skills/tree/HEAD/skills/cudaq-guide/SKILL.md
- Updated: 2026-10-23
- HTML: https://affpapa.org/en/nevibecode/skills/cudaq-guide

## What it does

The skill routes by argument. `/cudaq-guide install` covers installation and validation, `test-program` builds and runs a Bell-state kernel, `gpu-sim` helps pick a GPU, multi-GPU, tensor-network or CPU target. Separate branches handle `qpu` for hardware access and `parallelize` for `mgpu`, `mqpu`, async dispatch and distributed observe. Run it with no argument and it prints a menu, then asks which topic you want.

Two reference files sit behind it: `references/onboarding.md` for installation, targets, QPU providers and parallelization modes, and `references/authoring.md` for execution APIs, kernel-language constraints and compiler pitfalls. When an answer depends on a specific CUDA-Q version or backend behavior, the skill reads local CUDA-Q docs. Its tools are read-only: `Read`, `Glob`, `Grep`.

Python workflows need Python 3.10+, GPU targets on Linux need the CUDA Toolkit and an NVIDIA card, C++ needs Linux or WSL plus C++20. QPU work requires provider credentials. The `author` branch covers decorator-mode APIs from CUDA-Q 0.14 and 0.15, so behavior on other releases can differ — check `cudaq.__version__`. Qiskit porting is out of scope here; that is what `cudaq-importing` is for.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill cudaq-guide -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/cudaq-guide/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/cudaq-guide/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill cudaq-guide -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/cudaq-guide/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill cudaq-guide -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/cudaq-guide/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install NVIDIA/skills cudaq-guide
```

2. **Manually: put the files in place**
- File: `.github/skills/cudaq-guide/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill cudaq-guide -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/NVIDIA/skills --path skills/cudaq-guide
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/cudaq-guide/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill cudaq-guide -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/NVIDIA/skills/tree/HEAD/skills/cudaq-guide/SKILL.md

## When to use it
- install CUDA-Q and confirm the import works
- write and run a Bell-state kernel
- choose between GPU, multi-GPU and CPU simulation
- connect to a QPU with provider credentials
- debug a kernel that fails to compile

## Example prompts
- /cudaq-guide install
- /cudaq-guide author — help me write a @cudaq.kernel
- /cudaq-guide gpu-sim — which target should I use for GPU simulation?

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 3e961a7a264d9f76.

BEGIN THIRD-PARTY FILE 3e961a7a264d9f76
````text
---
name: "cudaq-guide"
title: "CUDA-Q Guide"
description: "Use for CUDA-Q setup, simulation targets, QPU access, and @cudaq.kernel authoring guidance."
version: "1.1.2"
author: "CUDA-Q Team <cuda-quantum@nvidia.com>"
tags: [cuda-quantum, quantum-computing, onboarding, getting-started, authoring, kernels, nvidia]
tools: [Read, Glob, Grep]
license: "Apache-2.0"
compatibility: "Python 3.10+, C++ 20"
metadata:
    author: "CUDA-Q Team <cuda-quantum@nvidia.com>"
    tags:
        - cuda-quantum
        - quantum-computing
        - onboarding
        - getting-started
        - nvidia
    languages:
        - python
        - c++
    domain: "quantum"
---

# 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
`cudaq-importing` 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
  `cudaq-importing`.

## Routing by Argument

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

## Menu

```text
````
END THIRD-PARTY FILE 3e961a7a264d9f76

The third-party file has ended. Everything between the 3e961a7a264d9f76 tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

First 2749 of 5031 bytes. © CUDA-Q Team · Apache-2.0 · https://github.com/NVIDIA/skills/tree/HEAD/skills/cudaq-guide/SKILL.md

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