qml.qnodes.QubitQNode

class QubitQNode(func, device, mutable=True, **kwargs)

Bases: pennylane.qnodes.jacobian.JacobianQNode

Quantum node for qubit parameter shift analytic differentiation

Attributes

h	step size for the finite difference method
interface	automatic differentiation interface used by the node, if any
order	order for the finite difference method

h

step size for the finite difference method

Type

float

interface

automatic differentiation interface used by the node, if any

Type

str, None

order

order for the finite difference method

Type

float

Methods

__call__(*args, **kwargs)	Wrapper for BaseQNode.evaluate().
append_operator(operator)	Append an operator to the global queue(s).
draw([charset, show_variable_names])	Draw the QNode as a circuit diagram.
evaluate(args, kwargs)	Evaluate the quantum function on the specified device.
evaluate_obs(obs, args, kwargs)	Evaluate the value of the given observables.
jacobian(args[, kwargs, wrt, method, options])	Compute the Jacobian of the QNode.
metric_tensor(args[, kwargs, diag_approx, …])	Evaluate the value of the metric tensor.
print_applied()	Prints the most recently applied operations from the QNode.
remove_operator(operator)	Remove an operator from the global queue(s) if it is in the queue(s).
to_autograd()	Attach the TensorFlow interface to the Jacobian QNode.
to_tf()	Attach the TensorFlow interface to the Jacobian QNode.
to_torch()	Attach the Torch interface to the Jacobian QNode.

__call__(*args, **kwargs)

Wrapper for BaseQNode.evaluate().

classmethod append_operator(operator)

Append an operator to the global queue(s).

Parameters

operator (Operator) – The Operator instance to be appended

draw(charset='unicode', show_variable_names=False)

Draw the QNode as a circuit diagram.

Consider the following circuit as an example:

@qml.qnode(dev)
def qfunc(a, w):
    qml.Hadamard(0)
    qml.CRX(a, wires=[0, 1])
    qml.Rot(w[0], w[1], w[2], wires=[1])
    qml.CRX(-a, wires=[0, 1])

    return qml.expval(qml.PauliZ(0) @ qml.PauliZ(1))

We can draw the circuit after it has been executed:

>>> result = qfunc(2.3, [1.2, 3.2, 0.7])
>>> print(qfunc.draw())
0: ──H──╭C────────────────────────────╭C─────────╭┤ ⟨Z ⊗ Z⟩
1: ─────╰RX(2.3)──Rot(1.2, 3.2, 0.7)──╰RX(-2.3)──╰┤ ⟨Z ⊗ Z⟩
>>> print(qfunc.draw(charset="ascii"))
0: --H--+C----------------------------+C---------+| <Z @ Z>
1: -----+RX(2.3)--Rot(1.2, 3.2, 0.7)--+RX(-2.3)--+| <Z @ Z>
>>> print(qfunc.draw(show_variable_names=True))
0: ──H──╭C─────────────────────────────╭C─────────╭┤ ⟨Z ⊗ Z⟩
1: ─────╰RX(a)──Rot(w[0], w[1], w[2])──╰RX(-1*a)──╰┤ ⟨Z ⊗ Z⟩

Parameters

• charset (str, optional) – The charset that should be used. Currently, “unicode” and “ascii” are supported.

• show_variable_names (bool, optional) – Show variable names instead of values.

Raises

• ValueError – If the given charset is not supported

• pennylane.QuantumFunctionError – Drawing is impossible because the underlying CircuitGraph has not yet been constructed

Returns

The circuit representation of the QNode

Return type

str

evaluate(args, kwargs)

Evaluate the quantum function on the specified device.

Parameters

• args (tuple[Any]) – positional arguments to the quantum function (differentiable)

• kwargs (dict[str, Any]) – auxiliary arguments (not differentiable)

Keyword Arguments

use_native_type (bool) – If True, return the result in whatever type the device uses internally, otherwise convert it into array[float]. Default: False.

Returns

output measured value(s)

Return type

float or array[float]

evaluate_obs(obs, args, kwargs)

Evaluate the value of the given observables.

Assumes construct() has already been called.

Parameters

• obs (Iterable[Observable]) – observables to measure

• args (tuple[Any]) – positional arguments to the quantum function (differentiable)

• kwargs (dict[str, Any]) – auxiliary arguments (not differentiable)

Returns

measured values

Return type

array[float]

jacobian(args, kwargs=None, *, wrt=None, method='best', options=None)

Compute the Jacobian of the QNode.

Returns the Jacobian of the parametrized quantum circuit encapsulated in the QNode. The Jacobian is returned as a two-dimensional array. The (possibly nested) input arguments of the QNode are flattened so the QNode can be interpreted as a simple \(\mathbb{R}^m \to \mathbb{R}^n\) function.

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, where n = len(wrt).

• Analytic method ('A'). Analytic, if implemented by the inheriting QNode.

• Best known method for each parameter ('best'): uses the analytic method if possible, otherwise finite difference.

• Device method ('device'): Delegates the computation of the Jacobian to the device executing the circuit. Only supported by devices that provide their own method for computing derivatives; support can be checked by querying the device capabilities: dev.capabilities()['provides_jacobian'] must return True. Examples of supported devices include the experimental "default.tensor.tf" device.

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

• args (nested Iterable[float] or float) – positional arguments to the quantum function (differentiable)

• kwargs (dict[str, Any]) – auxiliary arguments to the quantum function (not differentiable)

• wrt (Sequence[int] or None) – Indices of the flattened positional parameters with respect to which to compute the Jacobian. None means all the parameters. Note that you cannot compute the Jacobian with respect to the kwargs.

• method (str) – Jacobian computation method, in {'F', 'A', 'best', 'device'}, see above

• options (dict[str, Any]) –

  additional options for the computation methods

  • h (float): finite difference method step size

  • order (int): finite difference method order, 1 or 2

Returns

Jacobian, shape (n, len(wrt)), where n is the number of outputs returned by the QNode

Return type

array[float]

metric_tensor(args, kwargs=None, *, diag_approx=False, only_construct=False)

Evaluate the value of the metric tensor.

Parameters

• args (tuple[Any]) – positional (differentiable) arguments

• kwargs (dict[str, Any]) – auxiliary arguments

• diag_approx (bool) – iff True, use the diagonal approximation

• only_construct (bool) – Iff True, construct the circuits used for computing the metric tensor but do not execute them, and return None.

Returns

metric tensor

Return type

array[float]

print_applied()

Prints the most recently applied operations from the QNode.

classmethod remove_operator(operator)

Remove an operator from the global queue(s) if it is in the queue(s).

Parameters

operator (Operator) – The Operator instance to be removed

to_autograd()

Attach the TensorFlow interface to the Jacobian QNode.

Raises

QuantumFunctionError – if Autograd is not installed

to_tf()

Attach the TensorFlow interface to the Jacobian QNode.

Raises

QuantumFunctionError – if TensorFlow >= 1.12 is not installed

to_torch()

Attach the Torch interface to the Jacobian QNode.

Raises

QuantumFunctionError – if PyTorch is not installed