PennyLane Backend Configuration

The PennyLaneBackend enables executing HLQuantum arrays entirely via Xanadu's high-performance PennyLane ecosystem.

Installation

Ensure you have the required extra installed:

pip install hlquantum[pennylane]

This installs pennylane.

Default Qubits and Local Hardware

Without any configuration parameters, this uses default.qubit to generate rapid simulation on CPU processors. When running inside GPU devices, the "lightning.gpu" device enables highly optimized execution parameters using cuQuantum integration natively. Note that Xanadu plugin backends also leverage third-party device integrations transparently.

from hlquantum.backends import PennyLaneBackend
import hlquantum as hlq

# Instantiate an optimized lightning backend simulator with GPU integration
backend = PennyLaneBackend(device_name="lightning.gpu")
result = hlq.run(circuit, backend=backend, shots=1000)

PennyLane-Hosted Vendor Integrations

You can optionally run any supported PennyLane plugin with the device_kwargs. Access configurations, like Amazon Braket tasks running natively over PennyLane workflows or Xanadu Photonic Quantum Computers, automatically retrieve environment variables matching the underlying technology, standardizing their execution profiles within PennyLaneBackend workflows.

from hlquantum.backends import PennyLaneBackend

# Expose a separate QPU hardware via Xanadu PennyLane plugins.
backend = PennyLaneBackend(
    device_name="braket.aws.qubit",
    device_kwargs={
        "device_arn": "arn:aws:braket:::device/qpu/ionq/Aria-1",
        "s3_destination_folder": ("my-bucket", "prefix")
    }
)

When running on Xanadu's cloud platform, the XANADU_CLOUD_API_KEY token is required to interface with remote endpoints. Obtain an API key from Xanadu Cloud.