qml.ops.op_math.Controlled

class Controlled(base, control_wires, control_values=None, work_wires=None, do_queue=True, id=None)[source]

Bases: pennylane.ops.op_math.symbolicop.SymbolicOp

Symbolic operator denoting a controlled operator.

Parameters
  • base (Operator) – the operator that is controlled

  • control_wires (Any) – The wires to control on.

Keyword Arguments
  • control_values (Iterable[Bool]) – The values to control on. Must be the same length as control_wires. Defaults to True for all control wires. Provided values are converted to Bool internally.

  • work_wires (Any) – Any auxiliary wires that can be used in the decomposition

Note

This class, Controlled, denotes a controlled version of any individual operation. ControlledOp adds Operation specific methods and properties to the more general Controlled class.

The ControlledOperation currently constructed by the ctrl() transform wraps an entire tape and does not provide as many representations and attributes as Controlled, but ControlledOperation does decompose.

See also

ControlledOp, and ctrl()

Example

>>> base = qml.RX(1.234, 1)
>>> Controlled(base, (0, 2, 3), control_values=[True, False, True])
Controlled(RX(1.234, wires=[1]), control_wires=[0, 2, 3], control_values=[True, False, True])
>>> op = Controlled(base, 0, control_values=[0])
>>> op
Controlled(RX(1.234, wires=[1]), control_wires=[0], control_values=[0])

The operation has both standard Operator properties and Controlled specific properties:

>>> op.base
RX(1.234, wires=[1])
>>> op.data
[1.234]
>>> op.wires
<Wires = [0, 1]>
>>> op.control_wires
<Wires = [0]>
>>> op.target_wires
<Wires = [1]>

Control values are lists of booleans, indicating whether or not to control on the 0==False value or the 1==True wire.

>>> op.control_values
[0]

Provided control values are converted to booleans internally, so any “truthy” or “falsy” objects work.

>>> Controlled(base, ("a", "b", "c"), control_values=["", None, 5]).control_values
[False, False, True]

Representations for an operator are available if the base class defines them. Sparse matrices are available if the base class defines either a sparse matrix or only a dense matrix.

>>> np.set_printoptions(precision=4) # easier to read the matrix
>>> qml.matrix(op)
array([[0.8156+0.j    , 0.    -0.5786j, 0.    +0.j    , 0.    +0.j    ],
       [0.    -0.5786j, 0.8156+0.j    , 0.    +0.j    , 0.    +0.j    ],
       [0.    +0.j    , 0.    +0.j    , 1.    +0.j    , 0.    +0.j    ],
       [0.    +0.j    , 0.    +0.j    , 0.    +0.j    , 1.    +0.j    ]])
>>> qml.eigvals(op)
array([1.    +0.j    , 1.    +0.j    , 0.8156+0.5786j, 0.8156-0.5786j])
>>> print(qml.generator(op, format='observable'))
(-0.5) [Projector0 X1]
>>> op.sparse_matrix()
<4x4 sparse matrix of type '<class 'numpy.complex128'>'
            with 6 stored elements in Compressed Sparse Row format>

If the provided base matrix is an Operation, then the created object will be of type ControlledOp. This class adds some additional methods and properties to the basic Controlled class.

>>> type(op)
<class 'pennylane.ops.op_math.controlled_class.ControlledOp'>
>>> op.parameter_frequencies
[(0.5, 1.0)]

active_wires

Wires modified by the operator.

arithmetic_depth

Arithmetic depth of the operator.

base

The base operator.

batch_size

Batch size of the operator if it is used with broadcasted parameters.

control_values

Iterable[Bool].

control_wires

The control wires.

data

The trainable parameters

has_adjoint

bool(x) -> bool

has_decomposition

bool(x) -> bool

has_diagonalizing_gates

bool(x) -> bool

has_matrix

bool(x) -> bool

hash

Integer hash that uniquely represents the operator.

hyperparameters

Dictionary of non-trainable variables that this operation depends on.

id

Custom string to label a specific operator instance.

is_hermitian

This property determines if an operator is hermitian.

name

String for the name of the operator.

ndim_params

Number of dimensions per trainable parameter of the operator.

num_params

Number of trainable parameters that the operator depends on.

num_wires

Number of wires the operator acts on.

parameters

Trainable parameters that the operator depends on.

target_wires

The wires of the target operator.

wires

Wires that the operator acts on.

work_wires

Additional wires that can be used in the decomposition.

active_wires

Wires modified by the operator. This is the control wires followed by the target wires.

arithmetic_depth
base

The base operator.

batch_size

Batch size of the operator if it is used with broadcasted parameters.

The batch_size is determined based on ndim_params and the provided parameters for the operator. If (some of) the latter have an additional dimension, and this dimension has the same size for all parameters, its size is the batch size of the operator. If no parameter has an additional dimension, the batch size is None.

Returns

Size of the parameter broadcasting dimension if present, else None.

Return type

int or None

control_values

Iterable[Bool]. For each control wire, denotes whether to control on True or False.

control_wires

The control wires.

data

parameters of the operator

Type

list[Any]

has_adjoint
has_decomposition
has_diagonalizing_gates
has_matrix
hash
hyperparameters

Dictionary of non-trainable variables that this operation depends on.

Type

dict

id

Custom string to label a specific operator instance.

is_hermitian
name

String for the name of the operator.

ndim_params

Number of dimensions per trainable parameter of the operator.

By default, this property returns the numbers of dimensions of the parameters used for the operator creation. If the parameter sizes for an operator subclass are fixed, this property can be overwritten to return the fixed value.

Returns

Number of dimensions for each trainable parameter.

Return type

tuple

num_params
num_wires
parameters

Trainable parameters that the operator depends on.

target_wires

The wires of the target operator.

wires
work_wires

Additional wires that can be used in the decomposition. Not modified by the operation.

adjoint()

Create an operation that is the adjoint of this one.

compute_decomposition(*params[, wires])

Representation of the operator as a product of other operators (static method).

compute_diagonalizing_gates(*params, wires, …)

Sequence of gates that diagonalize the operator in the computational basis (static method).

compute_eigvals(*params, **hyperparams)

Eigenvalues of the operator in the computational basis (static method).

compute_matrix(*params, **hyperparams)

Representation of the operator as a canonical matrix in the computational basis (static method).

compute_sparse_matrix(*params, **hyperparams)

Representation of the operator as a sparse matrix in the computational basis (static method).

decomposition()

Representation of the operator as a product of other operators.

diagonalizing_gates()

Sequence of gates that diagonalize the operator in the computational basis.

eigvals()

Eigenvalues of the operator in the computational basis.

expand()

Returns a tape that has recorded the decomposition of the operator.

generator()

Generator of an operator that is in single-parameter-form.

label([decimals, base_label, cache])

A customizable string representation of the operator.

map_wires(wire_map)

Returns a copy of the current operator with its wires changed according to the given wire map.

matrix([wire_order])

Representation of the operator as a matrix in the computational basis.

pow(z)

A list of new operators equal to this one raised to the given power.

queue([context])

Append the operator to the Operator queue.

simplify()

Reduce the depth of nested operators to the minimum.

sparse_matrix([wire_order, format])

Representation of the operator as a sparse matrix in the computational basis.

terms()

Representation of the operator as a linear combination of other operators.

adjoint()[source]

Create an operation that is the adjoint of this one.

Adjointed operations are the conjugated and transposed version of the original operation. Adjointed ops are equivalent to the inverted operation for unitary gates.

Returns

The adjointed operation.

static compute_decomposition(*params, wires=None, **hyperparameters)

Representation of the operator as a product of other operators (static method).

\[O = O_1 O_2 \dots O_n.\]

Note

Operations making up the decomposition should be queued within the compute_decomposition method.

See also

decomposition().

Parameters
  • params (list) – trainable parameters of the operator, as stored in the parameters attribute

  • wires (Iterable[Any], Wires) – wires that the operator acts on

  • hyperparams (dict) – non-trainable hyperparameters of the operator, as stored in the hyperparameters attribute

Returns

decomposition of the operator

Return type

list[Operator]

static compute_diagonalizing_gates(*params, wires, **hyperparams)

Sequence of gates that diagonalize the operator in the computational basis (static method).

Given the eigendecomposition \(O = U \Sigma U^{\dagger}\) where \(\Sigma\) is a diagonal matrix containing the eigenvalues, the sequence of diagonalizing gates implements the unitary \(U^{\dagger}\).

The diagonalizing gates rotate the state into the eigenbasis of the operator.

Parameters
  • params (list) – trainable parameters of the operator, as stored in the parameters attribute

  • wires (Iterable[Any], Wires) – wires that the operator acts on

  • hyperparams (dict) – non-trainable hyperparameters of the operator, as stored in the hyperparameters attribute

Returns

list of diagonalizing gates

Return type

list[Operator]

static compute_eigvals(*params, **hyperparams)

Eigenvalues of the operator in the computational basis (static method).

If diagonalizing_gates are specified and implement a unitary \(U^{dagger}\), the operator can be reconstructed as

\[O = U \Sigma U^{\dagger},\]

where \(\Sigma\) is the diagonal matrix containing the eigenvalues.

Otherwise, no particular order for the eigenvalues is guaranteed.

See also

eigvals() and eigvals()

Parameters
  • params (list) – trainable parameters of the operator, as stored in the parameters attribute

  • hyperparams (dict) – non-trainable hyperparameters of the operator, as stored in the hyperparameters attribute

Returns

eigenvalues

Return type

tensor_like

static compute_matrix(*params, **hyperparams)

Representation of the operator as a canonical matrix in the computational basis (static method).

The canonical matrix is the textbook matrix representation that does not consider wires. Implicitly, this assumes that the wires of the operator correspond to the global wire order.

See also

matrix() and matrix()

Parameters
  • params (list) – trainable parameters of the operator, as stored in the parameters attribute

  • hyperparams (dict) – non-trainable hyperparameters of the operator, as stored in the hyperparameters attribute

Returns

matrix representation

Return type

tensor_like

static compute_sparse_matrix(*params, **hyperparams)

Representation of the operator as a sparse matrix in the computational basis (static method).

The canonical matrix is the textbook matrix representation that does not consider wires. Implicitly, this assumes that the wires of the operator correspond to the global wire order.

See also

sparse_matrix()

Parameters
  • params (list) – trainable parameters of the operator, as stored in the parameters attribute

  • hyperparams (dict) – non-trainable hyperparameters of the operator, as stored in the hyperparameters attribute

Returns

sparse matrix representation

Return type

scipy.sparse._csr.csr_matrix

decomposition()[source]

Representation of the operator as a product of other operators.

\[O = O_1 O_2 \dots O_n\]

A DecompositionUndefinedError is raised if no representation by decomposition is defined.

Returns

decomposition of the operator

Return type

list[Operator]

diagonalizing_gates()[source]

Sequence of gates that diagonalize the operator in the computational basis.

Given the eigendecomposition \(O = U \Sigma U^{\dagger}\) where \(\Sigma\) is a diagonal matrix containing the eigenvalues, the sequence of diagonalizing gates implements the unitary \(U^{\dagger}\).

The diagonalizing gates rotate the state into the eigenbasis of the operator.

A DiagGatesUndefinedError is raised if no representation by decomposition is defined.

Returns

a list of operators

Return type

list[Operator] or None

eigvals()[source]

Eigenvalues of the operator in the computational basis.

If diagonalizing_gates are specified and implement a unitary \(U^{dagger}\), the operator can be reconstructed as

\[O = U \Sigma U^{\dagger},\]

where \(\Sigma\) is the diagonal matrix containing the eigenvalues.

Otherwise, no particular order for the eigenvalues is guaranteed.

Note

When eigenvalues are not explicitly defined, they are computed automatically from the matrix representation. Currently, this computation is not differentiable.

A EigvalsUndefinedError is raised if the eigenvalues have not been defined and cannot be inferred from the matrix representation.

Returns

eigenvalues

Return type

tensor_like

expand()

Returns a tape that has recorded the decomposition of the operator.

Returns

quantum tape

Return type

QuantumTape

generator()[source]

Generator of an operator that is in single-parameter-form.

For example, for operator

\[U(\phi) = e^{i\phi (0.5 Y + Z\otimes X)}\]

we get the generator

>>> U.generator()
  (0.5) [Y0]
+ (1.0) [Z0 X1]

The generator may also be provided in the form of a dense or sparse Hamiltonian (using Hermitian and SparseHamiltonian respectively).

The default value to return is None, indicating that the operation has no defined generator.

label(decimals=None, base_label=None, cache=None)[source]

A customizable string representation of the operator.

Parameters
  • decimals=None (int) – If None, no parameters are included. Else, specifies how to round the parameters.

  • base_label=None (str) – overwrite the non-parameter component of the label

  • cache=None (dict) – dictionary that carries information between label calls in the same drawing

Returns

label to use in drawings

Return type

str

Example:

>>> op = qml.RX(1.23456, wires=0)
>>> op.label()
"RX"
>>> op.label(decimals=2)
"RX\n(1.23)"
>>> op.label(base_label="my_label")
"my_label"
>>> op.label(decimals=2, base_label="my_label")
"my_label\n(1.23)"
>>> op.inv()
>>> op.label()
"RX⁻¹"

If the operation has a matrix-valued parameter and a cache dictionary is provided, unique matrices will be cached in the 'matrices' key list. The label will contain the index of the matrix in the 'matrices' list.

>>> op2 = qml.QubitUnitary(np.eye(2), wires=0)
>>> cache = {'matrices': []}
>>> op2.label(cache=cache)
'U(M0)'
>>> cache['matrices']
[tensor([[1., 0.],
 [0., 1.]], requires_grad=True)]
>>> op3 = qml.QubitUnitary(np.eye(4), wires=(0,1))
>>> op3.label(cache=cache)
'U(M1)'
>>> cache['matrices']
[tensor([[1., 0.],
        [0., 1.]], requires_grad=True),
tensor([[1., 0., 0., 0.],
        [0., 1., 0., 0.],
        [0., 0., 1., 0.],
        [0., 0., 0., 1.]], requires_grad=True)]
map_wires(wire_map: dict)[source]

Returns a copy of the current operator with its wires changed according to the given wire map.

Parameters

wire_map (dict) – dictionary containing the old wires as keys and the new wires as values

Returns

new operator

Return type

Operator

matrix(wire_order=None)[source]

Representation of the operator as a matrix in the computational basis.

If wire_order is provided, the numerical representation considers the position of the operator’s wires in the global wire order. Otherwise, the wire order defaults to the operator’s wires.

If the matrix depends on trainable parameters, the result will be cast in the same autodifferentiation framework as the parameters.

A MatrixUndefinedError is raised if the matrix representation has not been defined.

See also

compute_matrix()

Parameters

wire_order (Iterable) – global wire order, must contain all wire labels from the operator’s wires

Returns

matrix representation

Return type

tensor_like

pow(z)[source]

A list of new operators equal to this one raised to the given power.

Parameters

z (float) – exponent for the operator

Returns

list[Operator]

queue(context=<class 'pennylane.queuing.QueuingManager'>)

Append the operator to the Operator queue.

simplify()pennylane.ops.op_math.controlled_class.Controlled[source]

Reduce the depth of nested operators to the minimum.

Returns

simplified operator

Return type

Operator

sparse_matrix(wire_order=None, format='csr')[source]

Representation of the operator as a sparse matrix in the computational basis.

If wire_order is provided, the numerical representation considers the position of the operator’s wires in the global wire order. Otherwise, the wire order defaults to the operator’s wires.

A SparseMatrixUndefinedError is raised if the sparse matrix representation has not been defined.

Parameters

wire_order (Iterable) – global wire order, must contain all wire labels from the operator’s wires

Returns

sparse matrix representation

Return type

scipy.sparse._csr.csr_matrix

terms()

Representation of the operator as a linear combination of other operators.

\[O = \sum_i c_i O_i\]

A TermsUndefinedError is raised if no representation by terms is defined.

Returns

list of coefficients \(c_i\) and list of operations \(O_i\)

Return type

tuple[list[tensor_like or float], list[Operation]]