qml.Hermitian

class Hermitian(A, wires, id=None)

Bases: pennylane.operation.Observable

An arbitrary Hermitian observable.

For a Hermitian matrix \(A\), the expectation command returns the value

\[\braket{A} = \braketT{\psi}{\cdots \otimes I\otimes A\otimes I\cdots}{\psi}\]

where \(A\) acts on the requested wires.

If acting on \(N\) wires, then the matrix \(A\) must be of size \(2^N\times 2^N\).

Details:

• Number of wires: Any

• Number of parameters: 1

• Gradient recipe: None

Parameters

• A (array or Sequence) – square hermitian matrix

• wires (Sequence[int] or int) – the wire(s) the operation acts on

• id (str or None) – String representing the operation (optional)

Attributes

arithmetic_depth	Arithmetic depth of the operator.
batch_size	Batch size of the operator if it is used with broadcasted parameters.
eigendecomposition	Return the eigendecomposition of the matrix specified by the Hermitian observable.
grad_method
has_adjoint
has_decomposition
has_diagonalizing_gates
has_generator
has_matrix
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	All observables must be hermitian
name	String for the name of the operator.
ndim_params	Number of dimensions per trainable parameter that the operator depends on.
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

Arithmetic depth of the 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

eigendecomposition

Return the eigendecomposition of the matrix specified by the Hermitian observable.

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

It transforms the input operator according to the wires specified.

Returns

dictionary containing the eigenvalues and the eigenvectors of the Hermitian observable

Return type

dict[str, array]

grad_method = 'F'

has_adjoint = False

has_decomposition = True

has_diagonalizing_gates = True

has_generator = False

has_matrix = True

hash

Integer hash that uniquely represents the operator.

Type

int

hyperparameters

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

Type

dict

id

Custom string to label a specific operator instance.

is_hermitian

All observables must be hermitian

name

String for the name of the operator.

ndim_params = (2,)

Number of dimensions per trainable parameter that the operator depends on.

Type

tuple[int]

num_params = 1

Number of trainable parameters that the operator depends on.

Type

int

num_wires: Union[int, pennylane.operation.WiresEnum] = -1

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.

Returns

wires

Return type

Wires

Methods

adjoint()	Create an operation that is the adjoint of this one.
compare(other)	Compares with another Hamiltonian, Tensor, or Observable, to determine if they are equivalent.
compute_decomposition(A, wires)	Decomposes a hermitian matrix as a sum of Pauli operators.
compute_diagonalizing_gates(eigenvectors, 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(A)	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()	Return the gate set that diagonalizes a circuit according to the specified Hermitian observable.
eigvals()	Return the eigenvalues of the specified Hermitian observable.
expand()	Returns a tape that contains 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])	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()

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.

compare(other)

Compares with another Hamiltonian, Tensor, or Observable, to determine if they are equivalent.

Observables/Hamiltonians are equivalent if they represent the same operator (their matrix representations are equal), and they are defined on the same wires.

Warning

The compare method does not check if the matrix representation of a Hermitian observable is equal to an equivalent observable expressed in terms of Pauli matrices. To do so would require the matrix form of Hamiltonians and Tensors be calculated, which would drastically increase runtime.

Returns

True if equivalent.

Return type

(bool)

Examples

>>> ob1 = qml.X(0) @ qml.Identity(1)
>>> ob2 = qml.Hamiltonian([1], [qml.X(0)])
>>> ob1.compare(ob2)
True
>>> ob1 = qml.X(0)
>>> ob2 = qml.Hermitian(np.array([[0, 1], [1, 0]]), 0)
>>> ob1.compare(ob2)
False

static compute_decomposition(A, wires)

Decomposes a hermitian matrix as a sum of Pauli operators.

Parameters

• A (array or Sequence) – hermitian matrix

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

Returns

decomposition of the hermitian matrix

Return type

list[Operator]

Examples

>>> op = qml.X(0) + qml.Y(1) + 2 * qml.X(0) @ qml.Z(3)
>>> op_matrix = qml.matrix(op)
>>> qml.Hermitian.compute_decomposition(op_matrix, wires=['a', 'b', 'aux'])
[(
      1.0 * (I('a') @ Y('b') @ I('aux'))
    + 1.0 * (X('a') @ I('b') @ I('aux'))
    + 2.0 * (X('a') @ I('b') @ Z('aux'))
)]
>>> op = np.array([[1, 1], [1, -1]]) / np.sqrt(2)
>>> qml.Hermitian.compute_decomposition(op, wires=0)
[(
      0.7071067811865475 * X(0)
    + 0.7071067811865475 * Z(0)
)]

static compute_diagonalizing_gates(eigenvectors, wires)

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

• eigenvectors (array) – eigenvectors of the operator, as extracted from op.eigendecomposition[“eigvec”].

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

Returns

list of diagonalizing gates

Return type

list[Operator]

Example

>>> A = np.array([[-6, 2 + 1j], [2 - 1j, 0]])
>>> _, evecs = np.linalg.eigh(A)
>>> qml.Hermitian.compute_diagonalizing_gates(evecs, wires=[0])
[QubitUnitary(tensor([[-0.94915323-0.j,  0.2815786 +0.1407893j ],
                      [ 0.31481445-0.j,  0.84894846+0.42447423j]], requires_grad=True), wires=[0])]

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

Operator.eigvals() and qml.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(A)

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()

Parameters

A (tensor_like) – hermitian matrix

Returns

canonical matrix

Return type

tensor_like

Example

>>> A = np.array([[6+0j, 1-2j],[1+2j, -1]])
>>> qml.Hermitian.compute_matrix(A)
[[ 6.+0.j  1.-2.j]
 [ 1.+2.j -1.+0.j]]

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()

Return the gate set that diagonalizes a circuit according to the specified Hermitian observable.

Returns

list containing the gates diagonalizing the Hermitian observable

Return type

list

eigvals()

Return the eigenvalues of the specified Hermitian observable.

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 Hermitian observable

Return type

array

expand()

Returns a tape that contains the decomposition of the operator.

Warning

This function is deprecated and will be removed in version 0.39. The same behaviour can be achieved simply through ‘qml.tape.QuantumScript(self.decomposition())’.

Returns

quantum tape

Return type

QuantumTape

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 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)

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

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)

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 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)

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()

Reduce the depth of nested operators to the minimum.

Returns

simplified operator

Return type

Operator

sparse_matrix(wire_order=None)

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.

See also

compute_sparse_matrix()

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]]

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