--- 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: "CUDA-Q" 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: "CUDA-Q" 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 ### 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