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CudaqGpu Backend

The CudaqCpu backend class allows you to access the CUDA-Q GPU-accelerated simulators. CUDA-Q is NVIDIA's open-source programming platform designed for hybrid quantum-classical computing. It allows integration of quantum processors (QPUs) alongside classical CPUs and GPUs, enabling them to work together to solve complex computational problems. CudaqGpu supports selecting specific simulation backends such as a state vector simulator or a tensor networks simulator. For more information, please refer to the CUDA-Q docs.

Selecting a Simulator

Three general purpose GPU simulators are available. Either chose target="nvidia" for state vector simulation, target="tensornet" for tensor network simulation, or target="tensornet-mps" for matrix-product-state simulation.

Running on Luna Servers

This simulated backend runs on Luna servers.

Initialization

Initialize the CudaqGpu backend with:

from luna_quantum.backends import CudaqGpu

backend = CudaqGpu(
    target='nvidia',
    option='fp32'
)

API-Reference

CudaqGpu

Bases: BaseCudaqBackend

CUDA-Q GPU Simulator.

Use a NVIDIA CUDA-Q GPU simulator for circuit execution on Aqarios servers.

You have the choice between the statevector simulator "nvidia", and two tensor network based simulators "tensornet" and "tensornet-mps". The floating point precision can be increased by setting option to "fp64".

For more information on the simulators pelase refer to the CUDA-Q documentation.

option class-attribute instance-attribute 

option: Literal['fp64', 'fp32'] = 'fp32'

provider property 

provider: str

Retrieve the name of the provider.

Returns:

Type Description
str

The name of the provider.

target class-attribute instance-attribute 

target: Literal['nvidia', 'tensornet', 'tensornet-mps'] = 'nvidia'