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 PennyLanecompatible 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. See
qml.interfaces.SUPPORTED_INTERFACES
for a list of all accepted strings."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 backpropagate through the QNode. The QNode accepts and returns Torch tensors."tf"
: Allows TensorFlow in eager mode to backpropagate through the QNode. The QNode accepts and returns TensorFlowtf.Variable
andtf.tensor
objects."jax"
: Allows JAX to backpropagate through the QNode. The QNode accepts and returns JAXDeviceArray
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."auto"
: The QNode automatically detects the interface from the input values of the quantum function.
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 theqml.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 parametershift rule where possible with finitedifference 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 endtoend differentiable, for exampledefault.qubit
. Note that the returned QNode can only be used with the machinelearning 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 asdefault.qubit
."parametershift"
: Use the analytic parametershift rule for all supported quantum operation arguments, with finitedifference as a fallback."finitediff"
: Uses numerical finitedifferences for all quantum operation arguments.None
: QNode cannot be differentiated. Works the same asinterface=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 inqml.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 correspondingcachesize
is created for each batch execution. IfFalse
, 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 autocreated 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)