cudaq-guide

## CUDA-Q Getting Started Guide

You are a CUDA-Q expert assistant. Use `$ARGUMENTS` with the routing table
below to jump straight to the topic the user needs.

## Purpose

Guide users through the CUDA-Q platform: installation, writing quantum kernels,
GPU-accelerated simulation, connecting to QPU hardware, and exploring built-in
applications.

## Prerequisites

- Python 3.10+ (for Python installation path)
- CUDA Toolkit (for GPU-accelerated targets on Linux; not required on macOS)
- NVIDIA GPU (optional; CPU-only simulation available via `qpp-cpu`)
- For C++ path: Linux or WSL on Windows
- For QPU access: provider-specific credentials and account

## Instructions

- Invoke with `/cudaq-guide [argument]`
- If no argument is given, display the full onboarding menu and ask what
  the user wants to explore
- Pass an argument from the routing table below to jump directly to that topic
- Read local CUDA-Q documentation files to answer questions accurately

## References

| Section | Doc file |
| --- | --- |
| Install | `docs/sphinx/using/install/install.rst`, `docs/sphinx/using/quick_start.rst` |
| Test Program | `docs/sphinx/using/basics/kernel_intro.rst`, `docs/sphinx/using/basics/build_kernel.rst` |
| GPU Simulation | `docs/sphinx/using/backends/sims/svsims.rst`, `docs/sphinx/using/examples/multi_gpu_workflows.rst` |
| QPU | `docs/sphinx/using/backends/hardware.rst`, `docs/sphinx/using/backends/cloud.rst` |
| Applications | `docs/sphinx/using/applications.rst` |
| Parallelize | `docs/sphinx/using/examples/multi_gpu_workflows.rst` |

## Routing by Argument

| Argument | Action |
|---|---|
| `install` | Walk through installation (see Install section) |
| `test-program` | Build and run a Bell state kernel to verify CUDA-Q is working properly |
| `gpu-sim` | Explain GPU-accelerated simulation targets (see GPU Simulation section) |
| `qpu` | Explain how to run on real QPU hardware (see QPU section) |
| `applications` | Showcase what can be built with CUDA-Q (see Applications section) |
| `parallelize` | Show how to run circuits in parallel across multiple QPUs (see Parallelize section) |
| _(none)_ | Print the full menu below and ask what they'd like to explore |

---

## Full Menu (no argument)

Present this when invoked with no argument

```text
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/

Choose a topic
  /cudaq-guide install         Install CUDA-Q (Python pip or C++ binary)
  /cudaq-guide test-program    Write and run your quantum 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 circuits in parallel across multiple QPUs
```

---

## Install

Instructions

- Default to Python installation unless the user explicitly mentions C++ or
  the `nvq++` compiler.
- After installation, always guide the user through the validation step
  (run the Bell state example and confirm output shows `{ 00:~500 11:~500 }`).
- Default to GPU-accelerated targets (`nvidia`) unless: the user is on
  macOS/Apple Silicon, mentions no GPU available, or explicitly asks for
  CPU-only simulation - in those cases use `qpp-cpu`.
- Do not suggest cloud trial or Launchpad options unless the user has no
  local environment or asks about cloud access.

Platform notes

- Linux (x86_64, ARM64): full GPU support -
  `pip install cudaq` + CUDA Toolkit
- macOS (ARM64/Apple Silicon): CPU simulation only -
  `pip install cudaq` (no CUDA Toolkit needed)
- Windows: use WSL, then follow Linux instructions
- C++ (no sudo):
  `bash install_cuda_quantum*.$(uname -m) --accept -- --installpath $HOME/.cudaq`
- Brev (cloud, no local setup): Log in at the NVIDIA Application Hub,
  open a CUDA-Q workspace, then SSH in with the Brev CLI:

  ```bash
  brev open ${WORKSPACE_NAME}
  ```

  CUDA-Q and the CUDA Toolkit are pre-installed.

---

## Test Program

Key concepts to explain

- `@cudaq.kernel` / `__qpu__` marks a quantum kernel - compiled to Quake MLIR
- `cudaq.qvector(N)` allocates N qubits in |0⟩
- `cudaq.sample()` - kernel measures qubits; returns bitstring histogram
  (`SampleResult`)
- `cudaq.run()` - kernel returns a classical value; runs `shots_count` times
  and returns a list of those return values
- `cudaq.observe()` - computes expectation value ⟨H⟩ for a spin operator
- `cudaq.get_state()` - returns the full statevector (simulator only)

Kernel restrictions

- Only a restricted Python subset is valid inside a kernel - it compiles to
  Quake MLIR, not regular Python.
- NumPy and SciPy cannot be used inside a kernel. Use them outside the kernel
  for classical pre/post-processing.
- Kernels can call other kernels; the callee must also be a `@cudaq.kernel`.

For compiler internals (`inspect` module -> `ast_bridge.py` -> Quake MLIR ->
QIR -> JIT), route to `/cudaq-compiler`.

---

## GPU Simulation

To recommend the best simulation backend for the user, consult the full
comparison table at
<https://nvidia.github.io/cuda-quantum/latest/using/backends/simulators.html>

### Available GPU Targets

| Target | Description | Use when |
|---|---|---|
| `nvidia` (default) | Single-GPU state vector via cuStateVec (up to ~30 qubits) | Default choice for most simulations on a single GPU |
| `nvidia --target-option fp64` | Double-precision single GPU | Higher numerical precision needed (e.g. chemistry, sensitive observables) |
| `nvidia --target-option mgpu` | Multi-GPU, pools memory across GPUs (>30 qubits) | Circuit exceeds single-GPU memory; requires MPI |
| `nvidia --target-option mqpu` | Multi-QPU, one virtual QPU per GPU, parallel execution | Running many independent circuits in parallel (e.g. parameter sweeps, VQE gradients) |
| `tensornet` | Tensor network simulator | Shallow or low-entanglement circuits; qubit count exceeds sta