NVIDIA · github-actions · Apr 17, 2026
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+---
+name: "cudaq-guide"
+title: "Cuda Quantum"
+description: "CUDA-Q onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications."
+version: "1.0.0"
+author: "Sachin Pisal <spisal@nvidia.com>"
+tags: [cuda-quantum, quantum-computing, onboarding, getting-started, nvidia]
+tools: [Read, Glob, Grep, Bash]
+license: "Apache License 2.0"
+compatibility: "Python 3.10+, C++ 20"
+metadata:
+    author: "Sachin Pisal <spisal@nvidia.com>"
+    tags:
+        - cuda-quantum
+        - quantum-computing
+        - onboarding
+        - getting-started
+        - nvidia
+    languages:
+        - python
+        - c++
+    domain: "quantum"
+---
+
+## CUDA-Q Getting Started Guide
+
+You are a CUDA-Q expert assistant. Guide the user through the CUDA-Q platform
+based on their `$ARGUMENTS`. If no argument is given, present the full
+onboarding menu.
+
+## 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
+
+Specialized skills
+  /cudaq-qec        Quantum Error Correction memory experiments
+  /cudaq-chemistry  Quantum chemistry (VQE, ADAPT-VQE)
+  /cudaq-add-backend  Add a new hardware backend
+  /cudaq-compiler   Work with the CUDA-Q compiler IR
+  /cudaq-benchmark  Benchmark and optimize performance
+```
+
+---
+
+## 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 statevector feasibility |
+| `qpp-cpu` | CPU-only fallback (OpenMP) | No GPU available; macOS; small circuits for testing |
+
+---
+
+## QPU
+
+When the user invokes this section, do not dump all providers at once.
+Instead, follow this two-step dialogue:
+
+Step 1 - ask which technology they want
+
+```text
+Which QPU technology are you targeting?
+  1. Ion trap       (IonQ, Quantinuum)
+  2. Superconducting (IQM, OQC, Anyon, TII, QCI)
+  3. Neutral atom   (QuEra, Infleqtion, Pasqal)
+  4. Cloud / multi-platform (AWS Braket, Scaleway)
+```
+
+Step 2 - once they pick a technology, ask which provider, then read the
+corresponding doc file and walk the user through it step by step.
+
+| Technology | Provider | Doc file |
+|---|---|---|
+| Ion trap | IonQ | `docs/sphinx/using/backends/hardware/iontrap.rst` (IonQ section) |
+| Ion trap | Quantinuum | `docs/sphinx/using/backends/hardware/iontrap.rst` (Quantinuum section) |
+| Superconducting | IQM | `docs/sphinx/using/backends/hardware/superconducting.rst` (IQM section) |
+| Superconducting | OQC | `docs/sphinx/using/backends/hardware/superconducting.rst` (OQC section) |
+| Superconducting | Anyon | `docs/sphinx/using/backends/hardware/superconducting.rst` (Anyon section) |
+| Superconducting | TII | `docs/sphinx/using/backends/hardware/superconducting.rst` (TII section) |
+| Superconducting | QCI | `docs/sphinx/using/backends/hardware/superconducting.rst` (QCI section) |
+| Neutral atom | Infleqtion | `docs/sphinx/using/backends/hardware/neutralatom.rst` (Infleqtion section) |
+| Neutral atom | QuEra | `docs/sphinx/using/backends/hardware/neutralatom.rst` (QuEra section) |
+| Neutral atom | Pasqal | `docs/sphinx/using/backends/hardware/neutralatom.rst` (Pasqal section) |
+| Cloud | AWS Braket | `docs/sphinx/using/backends/cloud/braket.rst` |
+| Cloud | Scaleway | `docs/sphinx/using/backends/cloud/scaleway.rst` |
+
+After walking through the provider steps, always close with
+
+- Test locally first with `emulate=True` before submitting to real hardware.
+- Use `cudaq.sample_async()` / `cudaq.observe_async()` for non-blocking submission.
+
+---
+
+## Applications
+
+CUDA-Q ships with ready-to-run application notebooks
+
+| Category | Examples |
+|---|---|
+| Optimization | QAOA, ADAPT-QAOA, MaxCut |
+| Chemistry | VQE, UCCSD, ADAPT-VQE -> see `/cudaq-chemistry` |
+| Error Correction | Surface codes, QEC memory -> see `/cudaq-qec` |
+| Algorithms | Grover's, Shor's, QFT, Deutsch-Jozsa, HHL |
+| ML | Quantum neural networks, kernel methods |
+| Simulation | Hamiltonian dynamics, Trotter evolution |
+| Finance | Portfolio optimization, Monte Carlo |
+
+Point to sub-skills for specialized topics
+
+- `/cudaq-qec` - full QEC memory experiment walkthrough
+- `/cudaq-chemistry` - VQE and ADAPT-VQE for molecular energies
+- `/cudaq-benchmark` - performance profiling and multi-GPU scaling
+
+---
+
+## Parallelize
+
+CUDA-Q supports two distinct multi-GPU parallelization strategies - pick based
+on what you are trying to scale.
+
+| Goal | Strategy | Target option |
+|---|---|---|
+| Single circuit too large for one GPU | Pool GPU memory | `nvidia --target-option mgpu` |
+| Many independent circuits at once | Run circuits in parallel | `nvidia --target-option mqpu` |
+| Large Hamiltonian expectation value | Distribute terms across GPUs | `mqpu` + `execution=cudaq.parallel.thread` |
+
+### Circuit batching with mqpu (`sample_async` / `observe_async`)
+
+The `mqpu` option maps one virtual QPU to each GPU. Dispatch circuits
+asynchronously with `qpu_id` to all GPUs simultaneously.
+
+```python
+import cudaq
+
+cudaq.set_target("nvidia", option="mqpu")
+n_qpus = cudaq.get_platform().num_qpus()
+
+futures = [
+    cudaq.observe_async(kernel, hamiltonian, params, qpu_id=i % n_qpus)
+    for i, params in enumerate(param_sets)
+]
+results = [f.get().expectation() for f in futures]
+```
+
+### Hamiltonian batching
+
+For a single kernel with a large Hamiltonian, add `execution=` to
+`cudaq.observe` — no other code change needed.
+
+```python
+# Single node, multiple GPUs
+result = cudaq.observe(kernel, hamiltonian, *args,
+                       execution=cudaq.parallel.thread)
+
+# Multi-node via MPI
+result = cudaq.observe(kernel, hamiltonian, *args,
+                       execution=cudaq.parallel.mpi)
+```
+
+See the docs above for complete working examples of both patterns.
+
+---
+
+## Limitations
+
+- GPU simulation requires Linux (x86_64 or ARM64); macOS is CPU-only
+- Multi-GPU `mgpu` target requires MPI
+- Kernel code must use a restricted Python subset; NumPy/SciPy are not
+  allowed inside kernels
+- QPU access requires provider-specific credentials and accounts
+
+## Troubleshooting
+
+- Import error after `pip install cudaq`: Ensure Python 3.10+ and a
+  supported OS (Linux or macOS)
+- No GPU detected: Verify CUDA Toolkit is installed and `nvidia-smi`
+  shows your GPU; fall back to `qpp-cpu`
+- Kernel compile error: Check that only supported Python constructs are
+  used inside `@cudaq.kernel`
+- QPU submission fails: Confirm credentials are set as environment
+  variables per the provider docs