qml.interfaces¶

Warning

Unless you are a PennyLane or plugin developer, you likely do not need to use these functions directly.

See the main interfaces page for more details on available interfaces.

Modules

This subpackage defines functions for interfacing devices’ execution capabilities with different machine learning libraries.

Execution functions and utilities¶

`execute`(tapes, device[, gradient_fn, …])	Execute a batch of tapes on a device in an autodifferentiable-compatible manner.
`cache_execute`(fn, cache[, pass_kwargs, …])	Decorator that adds caching to a function that executes multiple tapes on a device.
`set_shots`(device, shots)	Context manager to temporarily change the shots of a device.

`autograd`	This module contains functions for adding the Autograd interface to a PennyLane Device class.
`jax`	This module contains functions for adding the JAX interface to a PennyLane Device class.
`jax_jit`	This module contains functions for adding the JAX interface to a PennyLane Device class.
`tensorflow`	This module contains functions for adding the TensorFlow interface to a PennyLane Device class.
`tensorflow_autograph`	This module contains functions for adding the TensorFlow Autograph interface to a PennyLane Device class.
`torch`	This module contains functions for adding the PyTorch interface to a PennyLane Device class.

code/qml_interfaces

Download Python script

Download Notebook

View on GitHub