CUDA-Q

Description

CUDA-Q is an open-source quantum development platform that orchestrates the hardware and software needed to run useful, large-scale quantum computing applications. The platform’s hybrid programming model allows computation on GPU, CPU, and QPU resources in tandem from within a single quantum program. CUDA-Q is “qubit-agnostic”—seamlessly integrating with all QPUs and qubit modalities and offering GPU-accelerated simulations when adequate quantum hardware isn’t available.

Available Versions

The following versions of CUDA-Q are currently available:

0.12.0

• Runtime dependencies:
  • singularity/ce-3.11.0

CUDA-Q is available as a container; use singularity to build or run the applications. First load the singularity module with the following command:

module load singularity/ce-3.11.0

The container is located at /storage-apps/software/containers/cuda-q/cuda-quantum_cu12-0.12.0.sif.

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Best practice for using CUDA-Q

We recommend running CUDA-Q applications on GPUs. CUDA-Q quantum algorithm simulations can achieve a significant speedup on the GPU over the CPU.

Examples

Here are the example codes written in Python and C++.

PythonC++

import sys
import cudaq

print(f"Running on target {cudaq.get_target().name}")
qubit_count = int(sys.argv[1]) if 1 < len(sys.argv) else 2


@cudaq.kernel
def kernel():
    qubits = cudaq.qvector(qubit_count)
    h(qubits[0])
    for i in range(1, qubit_count):
        x.ctrl(qubits[0], qubits[i])
    mz(qubits)


result = cudaq.sample(kernel)
print(result)  # Example: { 11:500 00:500 }

To run the example, execute the following command after adding singularity to your environment:

singularity exec --nv /storage-apps/software/containers/cuda-q/cuda-quantum_cu12-0.12.0.sif python3 program.py

#include <cudaq.h>

__qpu__ void kernel(int qubit_count) {
  cudaq::qvector qubits(qubit_count);
  h(qubits[0]);
  for (auto i = 1; i < qubit_count; ++i) {
    cx(qubits[0], qubits[i]);
  }
  mz(qubits);
}

int main(int argc, char *argv[]) {
  auto qubit_count = 1 < argc ? atoi(argv[1]) : 2;
  auto result = cudaq::sample(kernel, qubit_count);
  result.dump(); // Example: { 11:500 00:500 }
}

After adding singularity to your environment, compile the code:

singularity exec --nv /storage-apps/software/containers/cuda-q/cuda-quantum_cu12-0.12.0.sif nvq++ program.cpp -o program.x

To run the example, execute the following command:

singularity exec --nv /storage-apps/software/containers/cuda-q/cuda-quantum_cu12-0.12.0.sif ./program.x

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You can find more examples in /storage-apps/software/cuda-q/examples/. It also contains Jupyter notebooks which can be run using the Jupyter Notebook - singularity app via the Open OnDemand platform. Specify the container path mentioned above in the form.

User guide

More information on CUDA-Q can be found here. The documentation and user guide are available here.

Created by: Jozef Federic, Marek Štekláč, Marek Štekláč