PennyLane-Strawberry Fields Plugin¶
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.
PennyLane-SF provides various Strawberry Fields devices for PennyLane:
Optimized simulator that supports only Gaussian operations and photon number resolving measurements.
Specialized simulator giving access to analytic gradients in Gaussian boson sampling.
TensorFlow simulator that supports backpropagation and all continuous-variable operations.
To see the PennyLane-SF plugin in action, you can 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,
dev = qml.device('strawberryfields.gaussian', wires=XXX)
'strawberryfields.fock' device is explicitly used in the
quantum neural net tutorial.
To filter tutorials that use a StrawberryFields device, use the “Strawberry Fields” filter on the right panel of the demos.