PennyLane-Strawberry Fields Plugin

Release

0.30.0-dev

The PennyLane-SF plugin integrates the StrawberryFields photonic quantum computing framework with PennyLane’s quantum machine learning capabilities.

PennyLane is a machine learning library for optimization and automatic differentiation of hybrid quantum-classical computations.

Strawberry Fields is a full-stack Python library for designing, simulating, and optimizing photonic quantum circuits.

Once the PennyLane-SF plugin is installed, the provided Strawberry Fields devices can be accessed straight away in PennyLane, without the need to import any additional packages.

Devices

PennyLane-SF provides various Strawberry Fields devices for PennyLane:

'strawberryfields.fock'

Full simulator that supports all continuous-variable operations.

'strawberryfields.gaussian'

Optimized simulator that supports only Gaussian operations and photon number resolving measurements.

'strawberryfields.remote'

Remote backends including access to continuous-variable hardware.

'strawberryfields.gbs'

Specialized simulator giving access to analytic gradients in Gaussian boson sampling.

'strawberryfields.tf'

TensorFlow simulator that supports backpropagation and all continuous-variable operations.

Note

The Strawberry Fields plugin only supports continuous-variable (CV) operations, such as Squeezing, or NumberOperator.

Tutorials

Check out these demos to see the PennyLane-SF plugin in action:

Plugins and Hybrid computation

Function fitting with a photonic quantum neural network

Quantum advantage with Gaussian Boson Sampling

You can also use any of the continuous-variable based demos from the PennyLane documentation, for example the tutorial on Gaussian transformations, and simply replace 'default.gaussian' with any of the available Strawberry Fields devices, such as 'strawberryfields.gaussian':

dev = qml.device('strawberryfields.gaussian', wires=XXX)

index

 

Download Python script

 

Download Notebook

 

View on GitHub