qml.ops.op_math.SProd¶

class SProd(scalar, base, id=None, _pauli_rep=None)[source]¶

Bases: pennylane.ops.op_math.symbolicop.ScalarSymbolicOp

Arithmetic operator representing the scalar product of an operator with the given scalar.

Parameters

scalar (float or complex) – the scale factor being multiplied to the operator.
base (Operator) – the operator which will get scaled.

Keyword Arguments

id (str or None) – id for the scalar product operator. Default is None.

Note

Currently this operator can not be queued in a circuit as an operation, only measured terminally.

See also

s_prod()

Example

>>> sprod_op = SProd(1.23, qml.X(0))
>>> sprod_op
1.23 * X(0)
>>> qml.matrix(sprod_op)
array([[0.  , 1.23],
       [1.23, 0.  ]])
>>> sprod_op.terms()
([1.23], [PauliX(wires=[0]])

Usage Details

The SProd operation can also be measured inside a qnode as an observable. If the circuit is parametrized, then we can also differentiate through the observable.

dev = qml.device("default.qubit", wires=1)

@qml.qnode(dev, diff_method="best")
def circuit(scalar, theta):
    qml.RX(theta, wires=0)
    return qml.expval(qml.s_prod(scalar, qml.Hadamard(wires=0)))

>>> scalar, theta = (1.2, 3.4)
>>> qml.grad(circuit, argnum=[0,1])(scalar, theta)
(array(-0.68362956), array(0.21683382))

Attributes

`arithmetic_depth`	Arithmetic depth of the operator.
`base`	The base operator.
`basis`
`batch_size`	Batch size of the operator if it is used with broadcasted parameters.
`data`	The trainable parameters
`has_adjoint`
`has_decomposition`
`has_diagonalizing_gates`	Whether the Operator returns defined diagonalizing gates.
`has_generator`
`has_matrix`	Whether or not the Operator returns a defined matrix.
`has_sparse_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`	If the base operator is hermitian and the scalar is real, then the scalar product 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.
`pauli_rep`	A `PauliSentence` representation of the Operator, or `None` if it doesn't have one.
`wires`	Wires that the operator acts on.

arithmetic_depth¶

base¶: The base operator.

basis¶

batch_size¶

data¶

has_adjoint = True¶

has_decomposition = False¶

has_diagonalizing_gates¶

Whether the Operator returns defined diagonalizing gates.

Type: Bool

has_generator = False¶

has_matrix¶

Whether or not the Operator returns a defined matrix.

Type: Bool

has_sparse_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¶: If the base operator is hermitian and the scalar is real, then the scalar product operator 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¶

Number of trainable parameters that the operator depends on. Usually 1 + the number of trainable parameters for the base op.

Returns: number of trainable parameters
Return type: int

num_wires¶: Number of wires the operator acts on.

parameters¶: Trainable parameters that the operator depends on.

pauli_rep¶: A PauliSentence representation of the Operator, or None if it doesn’t have one.

wires¶

Methods

`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`()	Return the eigenvalues of the specified operator.
`generator`()	Generator of an operator that is in single-parameter-form.
`label`([decimals, base_label, cache])	The label produced for the SProd op.
`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)	Returns the operator raised to a 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])	Computes, by default, a scipy.sparse.csr_matrix representation of this Tensor.
`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.

See also

diagonalizing_gates().

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.

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

Operator.matrix() and qml.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()¶

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.

See also

compute_decomposition().

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.

See also

compute_diagonalizing_gates().

Returns: a list of operators
Return type: list[Operator] or None

eigvals()[source]¶

Return the eigenvalues of the specified operator.

This method uses pre-stored eigenvalues for standard observables where possible and stores the corresponding eigenvectors from the eigendecomposition.

Returns: array containing the eigenvalues of the operator.
Return type: array

generator()¶

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 * Y(0) + Z(0) @ X(1)

The generator may also be provided in the form of a dense or sparse Hamiltonian (using Hamiltonian 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]¶: The label produced for the SProd op.

map_wires(wire_map)¶

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)¶

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 base matrix representation has not been defined.

See also

compute_matrix()

Parameters

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

Returns

matrix representation

Return type

tensor_like

pow(z)[source]¶: Returns the operator raised to a given power.

queue(context=<class 'pennylane.queuing.QueuingManager'>)¶: Append the operator to the Operator queue.

simplify()[source]¶

Reduce the depth of nested operators to the minimum.

Returns: simplified operator
Return type: Operator

sparse_matrix(wire_order=None)[source]¶

Computes, by default, a scipy.sparse.csr_matrix representation of this Tensor.

This is useful for larger qubit numbers, where the dense matrix becomes very large, while consisting mostly of zero entries.

Parameters: wire_order (Iterable) – Wire labels that indicate the order of wires according to which the matrix is constructed. If not provided, self.wires is used.
Returns: sparse matrix representation
Return type: scipy.sparse._csr.csr_matrix

terms()[source]¶

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]]

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