Cudaq Guide

References

Routing by Argument

Full Menu (no argument)

Present this when invoked with no argument

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:

  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

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

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.

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

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.

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.

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.

# 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