qml.qnode

qnode(func, device, interface='autograd', diff_method='best', expansion_strategy='gradient', max_expansion=10, mode='best', cache=True, cachesize=10000, max_diff=1, **gradient_kwargs)

Represents a quantum node in the hybrid computational graph.

A quantum node contains a quantum function (corresponding to a variational circuit) and the computational device it is executed on.

The QNode calls the quantum function to construct a QuantumTape instance representing the quantum circuit.

Parameters
  • func (callable) – a quantum function

  • device (Device) – a PennyLane-compatible device

  • interface (str) –

    The interface that will be used for classical backpropagation. This affects the types of objects that can be passed to/returned from the QNode:

    • "autograd": Allows autograd to backpropagate through the QNode. The QNode accepts default Python types (floats, ints, lists, tuples, dicts) as well as NumPy array arguments, and returns NumPy arrays.

    • "torch": Allows PyTorch to backpropogate through the QNode. The QNode accepts and returns Torch tensors.

    • "tf": Allows TensorFlow in eager mode to backpropogate through the QNode. The QNode accepts and returns TensorFlow tf.Variable and tf.tensor objects.

    • "jax": Allows JAX to backpropogate through the QNode. The QNode accepts and returns JAX DeviceArray objects.

    • None: The QNode accepts default Python types (floats, ints, lists, tuples, dicts) as well as NumPy array arguments, and returns NumPy arrays. It does not connect to any machine learning library automatically for backpropagation.

  • diff_method (str or gradient_transform) –

    The method of differentiation to use in the created QNode. Can either be a gradient_transform, which includes all quantum gradient transforms in the qml.gradients module, or a string. The following strings are allowed:

    • "best": Best available method. Uses classical backpropagation or the device directly to compute the gradient if supported, otherwise will use the analytic parameter-shift rule where possible with finite-difference as a fallback.

    • "device": Queries the device directly for the gradient. Only allowed on devices that provide their own gradient computation.

    • "backprop": Use classical backpropagation. Only allowed on simulator devices that are classically end-to-end differentiable, for example default.qubit. Note that the returned QNode can only be used with the machine-learning framework supported by the device.

    • "adjoint": Uses an adjoint method that reverses through the circuit after a forward pass by iteratively applying the inverse (adjoint) gate. Only allowed on supported simulator devices such as default.qubit.

    • "parameter-shift": Use the analytic parameter-shift rule for all supported quantum operation arguments, with finite-difference as a fallback.

    • "finite-diff": Uses numerical finite-differences for all quantum operation arguments.

    • None: QNode cannot be differentiated. Works the same as interface=None.

  • expansion_strategy (str) –

    The strategy to use when circuit expansions or decompositions are required.

    • gradient: The QNode will attempt to decompose the internal circuit such that all circuit operations are supported by the gradient method. Further decompositions required for device execution are performed by the device prior to circuit execution.

    • device: The QNode will attempt to decompose the internal circuit such that all circuit operations are natively supported by the device.

    The gradient strategy typically results in a reduction in quantum device evaluations required during optimization, at the expense of an increase in classical preprocessing.

  • max_expansion (int) – The number of times the internal circuit should be expanded when executed on a device. Expansion occurs when an operation or measurement is not supported, and results in a gate decomposition. If any operations in the decomposition remain unsupported by the device, another expansion occurs.

  • mode (str) – Whether the gradients should be computed on the forward pass (forward) or the backward pass (backward). Only applies if the device is queried for the gradient; gradient transform functions available in qml.gradients are only supported on the backward pass.

  • cache (bool or dict or Cache) – Whether to cache evaluations. This can result in a significant reduction in quantum evaluations during gradient computations. If True, a cache with corresponding cachesize is created for each batch execution. If False, no caching is used. You may also pass your own cache to be used; this can be any object that implements the special methods __getitem__(), __setitem__(), and __delitem__(), such as a dictionary.

  • cachesize (int) – The size of any auto-created caches. Only applies when cache=True.

  • max_diff (int) – If diff_method is a gradient transform, this option specifies the maximum number of derivatives to support. Increasing this value allows for higher order derivatives to be extracted, at the cost of additional (classical) computational overhead during the backwards pass.

Keyword Arguments

**kwargs – Any additional keyword arguments provided are passed to the differentiation method. Please refer to the qml.gradients module for details on supported options for your chosen gradient transform.

Example

QNodes can be created by decorating a quantum function:

>>> dev = qml.device("default.qubit", wires=1)
>>> @qml.qnode(dev)
... def circuit(x):
...     qml.RX(x, wires=0)
...     return expval(qml.PauliZ(0))

or by instantiating the class directly:

>>> def circuit(x):
...     qml.RX(x, wires=0)
...     return expval(qml.PauliZ(0))
>>> dev = qml.device("default.qubit", wires=1)
>>> qnode = qml.QNode(circuit, dev)