qml.QNode¶

class
QNode
(func, device, interface='auto', diff_method='best', expansion_strategy='gradient', max_expansion=10, grad_on_execution='best', mode=None, cache=True, cachesize=10000, max_diff=1, **gradient_kwargs)[source]¶ Bases:
object
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 JAXArray
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."hadamard"
: Use the analytic hadamard gradient test rule for all supported quantum operation arguments. More info is in the documentationqml.gradients.hadamard_grad
."finitediff"
: Uses numerical finitedifferences for all quantum operation arguments."spsa"
: Uses a simultaneous perturbation of all operation arguments to approximate the derivative.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.
grad_on_execution (bool, str) – Whether the gradients should be computed on the execution or not. Only applies if the device is queried for the gradient; gradient transform functions available in
qml.gradients
are only supported on the backward pass. The ‘best’ option chooses automatically between the two options and is default.mode (str) – Deprecated kwarg indicating 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. This argument does nothing with the new return system, and users should instead setgrad_on_execution
to indicate their desired behaviour.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 qml.expval(qml.PauliZ(0))
or by instantiating the class directly:
>>> def circuit(x): ... qml.RX(x, wires=0) ... return qml.expval(qml.PauliZ(0)) >>> dev = qml.device("default.qubit", wires=1) >>> qnode = qml.QNode(circuit, dev)
Parameter broadcasting
QNodes can be executed simultaneously for multiple parameter settings, which is called parameter broadcasting or parameter batching. We start with a simple example and briefly look at the scenarios in which broadcasting is possible and useful. Finally we give rules and conventions regarding the usage of broadcasting, together with some more complex examples. Also see the
Operator
documentation for implementation details.Example
Again consider the following
circuit
:>>> dev = qml.device("default.qubit", wires=1) >>> @qml.qnode(dev) ... def circuit(x): ... qml.RX(x, wires=0) ... return qml.expval(qml.PauliZ(0))
If we want to execute it at multiple values
x
, we may pass those as a onedimensional array to the QNode:>>> x = np.array([np.pi / 6, np.pi * 3 / 4, np.pi * 7 / 6]) >>> circuit(x) tensor([ 0.8660254 , 0.70710678, 0.8660254 ], requires_grad=True)
The resulting array contains the QNode evaluations at the single values:
>>> [circuit(x_val) for x_val in x] [tensor(0.8660254, requires_grad=True), tensor(0.70710678, requires_grad=True), tensor(0.8660254, requires_grad=True)]
In addition to the results being stacked into one
tensor
already, the broadcasted execution actually is performed in one simulation of the quantum circuit, instead of three sequential simulations.Benefits & Supported QNodes
Parameter broadcasting can be useful to simplify the execution syntax with QNodes. More importantly though, the simultaneous execution via broadcasting can be significantly faster than iterating over parameters manually. If we compare the execution time for the above QNode
circuit
between broadcasting and manual iteration for an input size of100
, we find a speedup factor of about \(30\). This speedup is a feature of classical simulators, but broadcasting may reduce the communication overhead for quantum hardware devices as well.A QNode supports broadcasting if all operators that receive broadcasted parameters do so. (Operators that are used in the circuit but do not receive broadcasted inputs do not need to support it.) A list of supporting operators is available in
supports_broadcasting
. Whether or not broadcasting delivers an increased performance will depend on whether the used device is a classical simulator and natively supports this. The latter can be checked with the capabilities of the device:>>> dev.capabilities()["supports_broadcasting"] True
If a device does not natively support broadcasting, it will execute broadcasted QNode calls by expanding the input arguments into separate executions. That is, every device can execute QNodes with broadcasting, but only supporting devices will benefit from it.
Usage
The first example above is rather simple. Broadcasting is possible in more complex scenarios as well, for which it is useful to understand the concept in more detail. The following rules and conventions apply:
There is at most one broadcasting axis
The broadcasted input has (exactly) one more axis than the operator(s) which receive(s) it would usually expect. For example, most operators expect a single scalar input and the broadcasted input correspondingly is a 1D array:
>>> x = np.array([1., 2., 3.]) >>> op = qml.RX(x, wires=0) # Additional axis of size 3.
An operator
op
that supports broadcasting indicates the expected number of axes–or dimensions–in its attributeop.ndim_params
. This attribute is a tuple with one integer per argument ofop
. The batch size of a broadcasted operator is stored inop.batch_size
:>>> op.ndim_params # RX takes one scalar input. (0,) >>> op.batch_size # The broadcasting axis has size 3. 3
The broadcasting axis is always the leading axis of an argument passed to an operator:
>>> from scipy.stats import unitary_group >>> U = np.stack([unitary_group.rvs(4) for _ in range(3)]) >>> U.shape # U stores three twoqubit unitaries, each of shape 4x4 (3, 4, 4) >>> op = qml.QubitUnitary(U, wires=[0, 1]) >>> op.batch_size 3
Stacking multiple broadcasting axes is not supported.
Multiple operators are broadcasted simultaneously
It is possible to broadcast multiple parameters simultaneously. In this case, the batch size of the broadcasting axes must match, and the parameters are combined like in Python’s
zip
function. Nonbroadcasted parameters do not need to be augmented manually but can simply be used as one would in individual QNode executions:dev = qml.device("default.qubit", wires=4) @qml.qnode(dev) def circuit(x, y, U): qml.QubitUnitary(U, wires=[0, 1, 2, 3]) qml.RX(x, wires=0) qml.RY(y, wires=1) qml.RX(x, wires=2) qml.RY(y, wires=3) return qml.expval(qml.PauliZ(0) @ qml.PauliX(1) @ qml.PauliZ(2) @ qml.PauliZ(3)) x = np.array([0.4, 2.1, 1.3]) y = 2.71 U = np.stack([unitary_group.rvs(16) for _ in range(3)])
This circuit takes three arguments, and the first two are used twice each.
x
andU
will lead to a batch size of3
for theRX
rotations and the multiqubit unitary, respectively. The inputy
is afloat
value and will be used together with all three values inx
andU
. We obtain three output values:>>> circuit(x, y, U) tensor([0.06939911, 0.26051235, 0.20361048], requires_grad=True)
This is equivalent to iterating over all broadcasted arguments using
zip
:>>> [circuit(x_val, y, U_val) for x_val, U_val in zip(x, U)] [tensor(0.06939911, requires_grad=True), tensor(0.26051235, requires_grad=True), tensor(0.20361048, requires_grad=True)]
In the same way it is possible to broadcast multiple arguments of a single operator, for example:
>>> qml.Rot.ndim_params # Rot takes three scalar arguments (0, 0, 0) >>> x = np.array([0.4, 2.3, 0.1]) # Broadcast the first argument with size 3 >>> y = 1.6 # Do not broadcast the second argument >>> z = np.array([1.2, 0.5, 2.5]) # Broadcast the third argument with size 3 >>> op = qml.Rot(x, y, z, wires=0) >>> op.batch_size 3
Broadcasting does not modify classical processing
Note that classical processing in QNodes will happen before broadcasting is taken into account. This means, that while operators always interpret the first axis as the broadcasting axis, QNodes do not necessarily do so:
@qml.qnode(dev) def circuit_unpacking(x): qml.RX(x[0], wires=0) qml.RY(x[1], wires=1) qml.RZ(x[2], wires=1) return qml.expval(qml.PauliZ(0) @ qml.PauliX(1)) x = np.array([[1, 2], [3, 4], [5, 6]])
The prepared parameter
x
has shape(3, 2)
, corresponding to the three operations and a batch size of2
:>>> circuit_unpacking(x) tensor([0.02162852, 0.30239696], requires_grad=True)
If we were to iterate manually over the parameter settings, we probably would put the batching axis in
x
first. This is not the behaviour with parameter broadcasting because it does not modify the unpacking step within the QNode, so thatx
is unpacked first and the unpacked elements are expected to contain the broadcasted parameters for each operator individually; if we attempted to put the broadcasting axis of size2
first, the indexing ofx
would fail in theRZ
rotation within the QNode.Attributes
The interface used by the QNode
The quantum tape
The quantum tape
The transform program used by the QNode.

interface
¶ The interface used by the QNode

qtape
¶ The quantum tape

tape
¶ The quantum tape

transform_program
¶ The transform program used by the QNode.
Warning
This is an experimental feature.
Methods
__call__
(*args, **kwargs)Call self as a function.
add_transform
(transform_container)Add a transform container to the transform program.
best_method_str
(device, interface)Similar to
get_best_method()
, except return the ‘best’ differentiation method in humanreadable format.construct
(args, kwargs)Call the quantum function with a tape context, ensuring the operations get queued.
get_best_method
(device, interface[, shots])Returns the ‘best’ differentiation method for a particular device and interface combination.
get_gradient_fn
(device, interface[, …])Determine the best differentiation method, interface, and device for a requested device, interface, and diff method.

add_transform
(transform_container)[source]¶ Add a transform container to the transform program.
Warning
This is an experimental feature.

static
best_method_str
(device, interface)[source]¶ Similar to
get_best_method()
, except return the ‘best’ differentiation method in humanreadable format.This method attempts to determine support for differentiation methods using the following order:
"device"
"backprop"
"parametershift"
"finitediff"
The first differentiation method that is supported (going from top to bottom) will be returned. Note that the SPSAbased and Hadamardbased gradient are not included here.
This method is intended only for debugging purposes. Otherwise,
get_best_method()
should be used instead. Parameters
device (Device) – PennyLane device
interface (str) – name of the requested interface
 Returns
The gradient function to use in humanreadable format.
 Return type
str

construct
(args, kwargs)[source]¶ Call the quantum function with a tape context, ensuring the operations get queued.

static
get_best_method
(device, interface, shots=None)[source]¶ Returns the ‘best’ differentiation method for a particular device and interface combination.
This method attempts to determine support for differentiation methods using the following order:
"device"
"backprop"
"parametershift"
"finitediff"
The first differentiation method that is supported (going from top to bottom) will be returned. Note that the SPSAbased and Hadamardbased gradients are not included here.
 Parameters
device (Device) – PennyLane device
interface (str) – name of the requested interface
 Returns
Tuple containing the
gradient_fn
,gradient_kwargs
, and the device to use when calling the execute function. Return type
tuple[str or gradient_transform, dict, Device

static
get_gradient_fn
(device, interface, diff_method='best', shots=None)[source]¶ Determine the best differentiation method, interface, and device for a requested device, interface, and diff method.
 Parameters
device (Device) – PennyLane device
interface (str) – name of the requested interface
diff_method (str or gradient_transform) – The requested method of differentiation. If a string, allowed options are
"best"
,"backprop"
,"adjoint"
,"device"
,"parametershift"
,"hadamard"
,"finitediff"
, or"spsa"
. A gradient transform may also be passed here.
 Returns
Tuple containing the
gradient_fn
,gradient_kwargs
, and the device to use when calling the execute function. Return type
tuple[str or gradient_transform, dict, Device