QNode¶
Module: pennylane

class
QNode
(func, device, cache=False)[source]¶ Quantum node in the hybrid computational graph.
Parameters:  func (callable) – a Python function containing
Operation
constructor calls, returning a tuple of measuredObservable
instances.  device (
Device
) – device to execute the function on  cache (bool) – If
True
, the quantum function used to generate the QNode will only be called to construct the quantum circuit once, on first execution, and this circuit structure (i.e., the placement of templates, gates, measurements, etc.) will be cached for all further executions. The circuit parameters can still change with every call. Only activate this feature if your quantum circuit structure will never change.

variable_ops
= None¶ Mapping from free parameter index to the list of
Operations
(in this circuit) that depend on it.The first element of the tuple is the index of the Operation in the program queue, the second the index of the parameter within the Operation.
Type: dict[int>list[(int, int)]]

construct
(args, kwargs=None)[source]¶ Constructs a representation of the quantum circuit.
The user should never have to call this method.
This method is called automatically the first time
QNode.evaluate()
orQNode.jacobian()
is called. It executes the quantum function, stores the resulting sequence ofOperation
instances, and creates the variable mapping.Parameters:  args (tuple) – Represent the free parameters passed to the circuit.
Here we are not concerned with their values, but with their structure.
Each free param is replaced with a
Variable
instance.  kwargs (dict) – Additional keyword arguments may be passed to the quantum circuit function, however PennyLane does not support differentiating with respect to keyword arguments. Instead, keyword arguments are useful for providing data or ‘placeholders’ to the quantum circuit function.
 args (tuple) – Represent the free parameters passed to the circuit.
Here we are not concerned with their values, but with their structure.
Each free param is replaced with a

construct_metric_tensor
(args, **kwargs)[source]¶ Create metric tensor subcircuits for each parameter.
If the parameter appears in a gate \(G\), the subcircuit contains all gates which precede \(G\), and \(G\) is replaced by the variance value of its generator.
Parameters: args (tuple) – Represent the free parameters passed to the circuit. Here we are not concerned with their values, but with their structure. Each free param is replaced with a Variable
instance.Keyword Arguments: diag_approx (bool) – If True
, forces the diagonal approximation. Default isFalse
.Note
Additional keyword arguments may be passed to the quantum circuit function, however PennyLane does not support differentiating with respect to keyword arguments. Instead, keyword arguments are useful for providing data or ‘placeholders’ to the quantum circuit function.

evaluate
(**kwargs)¶ Evaluates the quantum function on the specified device.
Parameters: args (tuple) – input parameters to the quantum function Returns: output measured value(s) Return type: float, array[float]

metric_tensor
(*args, **kwargs)[source]¶ Evaluate the value of the metric tensor.
Parameters:  args – qfunc positional arguments
 kwargs – qfunc keyword arguments
Keyword Arguments: diag_approx (bool) – If
True
, forces the diagonal approximation. Default isFalse
.Returns: measured values
Return type: array[float]

evaluate_obs
(obs, args, **kwargs)[source]¶ Evaluate the value of the given observables.
Assumes
construct()
has already been called.Parameters:  obs (Iterable[Observable]) – observables to measure
 args (array[float]) – circuit input parameters
Returns: measured values
Return type: array[float]

jacobian
(params, which=None, *, method='B', h=1e07, order=1, **kwargs)[source]¶ Compute the Jacobian of the QNode.
Returns the Jacobian of the parametrized quantum circuit encapsulated in the QNode.
The Jacobian can be computed using several methods:
Finite differences (
'F'
). The first order method evaluates the circuit at \(n+1\) points of the parameter space, the second order method at \(2n\) points, wheren = len(which)
.Analytic method (
'A'
). Works for all oneparameter gates where the generator only has two unique eigenvalues; this includes oneparameter qubit gates, as well as Gaussian circuits of order one or two. Additionally, can be used in CV systems for Gaussian circuits containing first and secondorder observables.The circuit is evaluated twice for each incidence of each parameter in the circuit.
Best known method for each parameter (
'B'
): uses the analytic method if possible, otherwise finite difference.
Note
The finite difference method is sensitive to statistical noise in the circuit output, since it compares the output at two points infinitesimally close to each other. Hence the ‘F’ method requires exact expectation values, i.e., analytic=True in simulation plugins.
Parameters:  params (nested Sequence[Number], Number) – point in parameter space at which to evaluate the gradient
 which (Sequence[int], None) – return the Jacobian with respect to these parameters. None (the default) means with respect to all parameters. Note that keyword arguments to the QNode are always treated as fixed values and not included in the Jacobian calculation.
 method (str) – Jacobian computation method, see above.
Keyword Arguments:  h (float) – finite difference method step size
 order (int) – finite difference method order, 1 or 2
 shots (int) – How many times the circuit should be evaluated (or sampled) to estimate the expectation values.
Returns: Jacobian matrix, with shape
(n_out, len(which))
, wherelen(which)
is the number of free parameters, andn_out
is the number of expectation values returned by the QNode.Return type: array[float]
 func (callable) – a Python function containing