qml.QSVT¶

class QSVT(UA, projectors)[source]¶

Bases: Operation

Implements the quantum singular value transformation (QSVT) circuit.

Note

This template allows users to define hardware-compatible block encoding and projector-controlled phase shift circuits. For a QSVT implementation that is tailored to work directly with an input matrix and a transformation polynomial see qsvt().

Given an Operator \(U\), which block encodes the matrix \(A\), and a list of projector-controlled phase shift operations \(\vec{\Pi}_\phi\), this template applies a circuit for the quantum singular value transformation as follows.

When the number of projector-controlled phase shifts is even (\(d\) is odd), the QSVT circuit is defined as:

\[U_{QSVT} = \tilde{\Pi}_{\phi_1}U\left[\prod^{(d-1)/2}_{k=1}\Pi_{\phi_{2k}}U^\dagger \tilde{\Pi}_{\phi_{2k+1}}U\right]\Pi_{\phi_{d+1}}.\]

And when the number of projector-controlled phase shifts is odd (\(d\) is even):

\[U_{QSVT} = \left[\prod^{d/2}_{k=1}\Pi_{\phi_{2k-1}}U^\dagger\tilde{\Pi}_{\phi_{2k}}U\right] \Pi_{\phi_{d+1}}.\]

This circuit applies a polynomial transformation (\(Poly^{SV}\)) to the singular values of the block encoded matrix:

\[\begin{split}\begin{align} U_{QSVT}(A, \vec{\phi}) &= \begin{bmatrix} Poly^{SV}(A) & \cdot \\ \cdot & \cdot \end{bmatrix}. \end{align}\end{split}\]

Parameters:

UA (Operator) – the block encoding circuit, specified as an Operator, like BlockEncode
projectors (Sequence[Operator]) – a list of projector-controlled phase shifts that implement the desired polynomial

Raises:

ValueError – if the input block encoding is not an operator

Example

To implement QSVT in a circuit, we can use the following method:

>>> dev = qml.device("default.qubit", wires=[0])
>>> block_encoding = qml.Hadamard(wires=0)  # note H is a block encoding of 1/sqrt(2)
>>> phase_shifts = [qml.RZ(-2 * theta, wires=0) for theta in (1.23, -0.5, 4)]  # -2*theta to match convention

>>> @qml.qnode(dev)
... def example_circuit():
...     qml.QSVT(block_encoding, phase_shifts)
...     return qml.expval(qml.Z(0))
...

>>> example_circuit()
np.float64(0.5403...)

We can visualize the circuit as follows:

>>> print(qml.draw(example_circuit)())
0: ──QSVT─┤  <Z>

To see the implementation details, we can expand the circuit:

>>> q_script = qml.tape.QuantumScript(ops=[qml.QSVT(block_encoding, phase_shifts)])
>>> print(q_script.expand().draw(decimals=2))
0: ──RZ(-2.46)──(H†)@RZ(1.00)@H──RZ(-8.00)─┤

See the Usage Details section for more examples on implementing QSVT with different block encoding methods.

Usage Details

The QSVT operation can be used with different block encoding methods, depending on the initial operator for which the singular value transformation is applied and the desired backend device. Examples are provided below.

If we want to transform the singular values of a matrix, the matrix can be block-encoded with either the BlockEncode or FABLE operations. Note that BlockEncode is more efficient on simulator devices but it cannot be used with hardware backends because it currently has no gate decomposition. The FABLE operation is less efficient on simulator devices but is hardware compatible.

The following example applies the polynomial \(p(x) = -x + 0.5x^3 + 0.5x^5\) to an arbitrary hermitian matrix using BlockEncode for block encoding.

poly = np.array([0, -1, 0, 0.5, 0, 0.5])
angles = qml.poly_to_angles(poly, "QSVT")
input_matrix = np.array([[0.2, 0.1], [0.1, -0.1]])

wires = [0, 1]
block_encode = qml.BlockEncode(input_matrix, wires=wires)
projectors = [
    qml.PCPhase(angles[i], dim=len(input_matrix), wires=wires)
    for i in range(len(angles))
]

dev = qml.device("default.qubit")
@qml.qnode(dev)
def circuit():
    qml.QSVT(block_encode, projectors)
    return qml.state()

>>> circuit() 
array([-0.1942+0.6665j, -0.0979+0.3583j,  0.332 -0.5105j, -0.0955+0.0104j])

If we want to transform the singular values of a linear combination of unitaries, e.g., a Hamiltonian, it can be block-encoded with operations such as PrepSelPrep or Qubitization. Note that both of these operations have a gate decomposition and can be implemented on hardware. The following example applies the polynomial \(p(x) = -x + 0.5x^3 + 0.5x^5\) to the Hamiltonian \(H = 0.1X_3 - 0.7X_3Z_4 - 0.2Z_3Y_4\), block-encoded with PrepSelPrep.

poly = np.array([0, -1, 0, 0.5, 0, 0.5])
H = 0.1 * qml.X(2) - 0.7 * qml.X(2) @ qml.Z(3) - 0.2 * qml.Z(2)

control_wires = [0, 1]
block_encode = qml.PrepSelPrep(H, control=control_wires)
angles = qml.poly_to_angles(poly, "QSVT")

projectors = [
    qml.PCPhase(angles[i], dim=2 ** len(H.wires), wires=control_wires + H.wires)
    for i in range(len(angles))
]

dev = qml.device("default.qubit")

@qml.qnode(dev)
def circuit():
    qml.QSVT(block_encode, projectors)
    return qml.state()

>>> circuit() 
array([ 1.44000000e-01+1.01511390e-01j,  0.00000000e+00+0.00000000e+00j,
        4.32000000e-01+3.04534169e-01j,  0.00000000e+00+0.00000000e+00j,
        -4.14503215e-17+7.27402636e-17j,  0.00000000e+00+0.00000000e+00j,
        5.59003542e-01+9.65699229e-02j,  0.00000000e+00+0.00000000e+00j,
        4.22566958e-01+7.30000000e-02j,  0.00000000e+00+0.00000000e+00j,
        -3.16925218e-01-5.47500000e-02j,  0.00000000e+00+0.00000000e+00j,
        5.20486781e-18-4.91300614e-17j,  0.00000000e+00+0.00000000e+00j,
        -2.79501771e-01-4.82849614e-02j,  0.00000000e+00+0.00000000e+00j])

Attributes

`arithmetic_depth`	Arithmetic depth of the operator.
`basis`	The basis of an operation, or for controlled gates, of the target operation.
`batch_size`	Batch size of the operator if it is used with broadcasted parameters.
`control_wires`	Control wires of the operator.
`data`	Flattened list of operator data in this QSVT operation.
`grad_method`	Gradient computation method.
`grad_recipe`	Gradient recipe for the parameter-shift method.
`has_adjoint`
`has_decomposition`
`has_diagonalizing_gates`
`has_generator`
`has_matrix`
`has_qfunc_decomposition`
`has_sparse_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`	This property determines if an operator is verified to be Hermitian.
`is_verified_hermitian`	This property determines if an operator is verified to be 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.
`parameter_frequencies`	Returns the frequencies for each operator parameter with respect to an expectation value of the form \(\langle \psi \| U(\mathbf{p})^\dagger \hat{O} U(\mathbf{p})\|\psi\rangle\).
`parameters`	Trainable parameters that the operator depends on.
`pauli_rep`	A `PauliSentence` representation of the Operator, or `None` if it doesn't have one.
`resource_keys`	The set of parameters that affects the resource requirement of the operator.
`resource_params`	A dictionary containing the minimal information needed to compute a resource estimate of the operator's decomposition.
`wires`	Wires that the operator acts on.

arithmetic_depth¶: Arithmetic depth of the operator.

basis¶

The basis of an operation, or for controlled gates, of the target operation. If not None, should take a value of "X", "Y", or "Z".

For example, X and CNOT have basis = "X", whereas ControlledPhaseShift and RZ have basis = "Z".

Type:: str or None

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_wires¶

Control wires of the operator.

For operations that are not controlled, this is an empty Wires object of length 0.

Returns:: The control wires of the operation.
Return type:: Wires

data¶

Flattened list of operator data in this QSVT operation.

This ensures that the backend of a QuantumScript which contains a QSVT operation can be inferred with respect to the types of the QSVT block encoding and projector-controlled phase shift data.

grad_method = None¶: Gradient computation method.

grad_recipe = None¶

Gradient recipe for the parameter-shift method.

This is a tuple with one nested list per operation parameter. For parameter \(\phi_k\), the nested list contains elements of the form \([c_i, a_i, s_i]\) where \(i\) is the index of the term, resulting in a gradient recipe of

\[\frac{\partial}{\partial\phi_k}f = \sum_{i} c_i f(a_i \phi_k + s_i).\]

If None, the default gradient recipe containing the two terms \([c_0, a_0, s_0]=[1/2, 1, \pi/2]\) and \([c_1, a_1, s_1]=[-1/2, 1, -\pi/2]\) is assumed for every parameter.

Type:: tuple(Union(list[list[float]], None)) or None

has_adjoint = False¶

has_decomposition = True¶

has_diagonalizing_gates = False¶

has_generator = False¶

has_matrix = True¶

has_qfunc_decomposition = False¶

has_sparse_matrix = False¶

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¶

This property determines if an operator is verified to be Hermitian.

Warning

The is_hermitian property is deprecated and has been renamed to is_verified_hermitian as it better reflects the functionality of this property. The deprecated access through is_hermitian will be removed in PennyLane v0.45. Alternatively, consider using the is_hermitian() function instead as it provides a more reliable check for hermiticity. Please be aware that it comes with a higher computational cost.

Note

This property provides a fast, non-exhaustive check used for internal optimizations. It relies on quick, provable shortcuts (e.g., operator properties) rather than a full, computationally expensive check.

For a definitive check, use the pennylane.is_hermitian() function. Please note that this comes with increased computational cost.

Returns:: The property will return True if the operator is guaranteed to be Hermitian and False if the check is inconclusive and the operator may or may not be Hermitian.
Return type:: bool

Consider this operator,

>>> op = (qml.X(0) @ qml.Y(0) - qml.X(0) @ qml.Z(0)) * 1j

In this case, Hermicity cannot be verified and leads to an inconclusive result:

>>> op.is_verified_hermitian # inconclusive
False

However, using pennylane.is_hermitian() will give the correct answer:

>>> qml.is_hermitian(op) # definitive
True

is_verified_hermitian¶

This property determines if an operator is verified to be Hermitian.

Note

This property provides a fast, non-exhaustive check used for internal optimizations. It relies on quick, provable shortcuts (e.g., operator properties) rather than a full, computationally expensive check.

For a definitive check, use the pennylane.is_hermitian() function. Please note that this comes with increased computational cost.

Returns:: The property will return True if the operator is guaranteed to be Hermitian and False if the check is inconclusive and the operator may or may not be Hermitian.
Return type:: bool

Consider this operator,

>>> op = (qml.X(0) @ qml.Y(0) - qml.X(0) @ qml.Z(0)) * 1j

In this case, Hermicity cannot be verified and leads to an inconclusive result:

>>> op.is_verified_hermitian # inconclusive
False

However, using pennylane.is_hermitian() will give the correct answer:

>>> qml.is_hermitian(op) # definitive
True

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.

By default, this property returns as many parameters as were used for the operator creation. If the number of parameters for an operator subclass is fixed, this property can be overwritten to return the fixed value.

Returns:: number of parameters
Return type:: int

num_wires = None¶: Number of wires the operator acts on.

parameter_frequencies¶

Returns the frequencies for each operator parameter with respect to an expectation value of the form \(\langle \psi | U(\mathbf{p})^\dagger \hat{O} U(\mathbf{p})|\psi\rangle\).

These frequencies encode the behaviour of the operator \(U(\mathbf{p})\) on the value of the expectation value as the parameters are modified. For more details, please see the pennylane.fourier module.

Returns:: Tuple of frequencies for each parameter. Note that only non-negative frequency values are returned.
Return type:: list[tuple[int or float]]

Example

>>> op = qml.CRot(0.4, 0.1, 0.3, wires=[0, 1])
>>> op.parameter_frequencies
[(0.5, 1.0), (0.5, 1.0), (0.5, 1.0)]

For operators that define a generator, the parameter frequencies are directly related to the eigenvalues of the generator:

>>> op = qml.ControlledPhaseShift(0.1, wires=[0, 1])
>>> op.parameter_frequencies
[(1,)]
>>> gen = qml.generator(op, format="observable")
>>> gen_eigvals = qml.eigvals(gen)
>>> qml.gradients.eigvals_to_frequencies(tuple(gen_eigvals))
(np.float64(1.0),)

For more details on this relationship, see eigvals_to_frequencies().

parameters¶: Trainable parameters that the operator depends on.

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

resource_keys = {'UA', 'projectors'}¶

The set of parameters that affects the resource requirement of the operator.

All decomposition rules for this operator class are expected to have a resource function that accepts keyword arguments that match these keys exactly. The resource_rep() function will also expect keyword arguments that match these keys when called with this operator type.

The default implementation is an empty set, which is suitable for most operators.

See also

resource_params()

resource_params¶

wires¶

Wires that the operator acts on.

Returns:: wires
Return type:: Wires

Methods

`adjoint`()	Create an operation that is the adjoint of this one.
`compute_decomposition`(*_data, UA, ...)	Representation of the operator as a product of other operators.
`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`(args, *kwargs)	Representation of the operator as a canonical matrix in the computational basis (static method).
`compute_qfunc_decomposition`(*args, ...)	Experimental method to compute the dynamic decomposition of the operator with program capture enabled.
`compute_sparse_matrix`(*params[, format])	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.
`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.
`single_qubit_rot_angles`()	The parameters required to implement a single-qubit gate as an equivalent `Rot` gate, up to a global phase.
`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()¶

Create an operation that is the adjoint of this one. Used to simplify Adjoint operators constructed by adjoint().

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

Operator.adjoint can be optionally defined by Operator developers, while adjoint() is the entry point for constructing generic adjoint representations.

Returns:: The adjointed operation.

>>> class MyClass(qml.operation.Operator):
...
...     def adjoint(self):
...         return self
...
>>> op = qml.adjoint(MyClass(wires=0))
>>> op
Adjoint(MyClass(wires=[0]))
>>> op.decomposition()
[MyClass(wires=[0])]
>>> op.simplify()
MyClass(wires=[0])

static compute_decomposition(*_data, UA, projectors, **_kwargs)[source]¶

Representation of the operator as a product of other operators.

The QSVT is decomposed into alternating block encoding and projector-controlled phase shift operators. This is defined by the following equations, where \(U\) is the block encoding operation and both \(\Pi_\phi\) and \(\tilde{\Pi}_\phi\) are projector-controlled phase shifts with angle \(\phi\).

When the number of projector-controlled phase shifts is even (\(d\) is odd), the QSVT circuit is defined as:

\[U_{QSVT} = \Pi_{\phi_1}U\left[\prod^{(d-1)/2}_{k=1}\Pi_{\phi_{2k}}U^\dagger \tilde{\Pi}_{\phi_{2k+1}}U\right]\Pi_{\phi_{d+1}}.\]

And when the number of projector-controlled phase shifts is odd (\(d\) is even):

\[U_{QSVT} = \left[\prod^{d/2}_{k=1}\Pi_{\phi_{2k-1}}U^\dagger\tilde{\Pi}_{\phi_{2k}}U\right] \Pi_{\phi_{d+1}}.\]

See also

decomposition().

Parameters:

UA (Operator) – the block encoding circuit, specified as a Operator
projectors (list[Operator]) – a list of projector-controlled phase shift circuits that implement the desired polynomial

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(*args, **kwargs)[source]¶

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_qfunc_decomposition(*args, **hyperparameters)¶

Experimental method to compute the dynamic decomposition of the operator with program capture enabled.

When the program capture feature is enabled with qml.capture.enable(), the decomposition of the operator is computed with this method if it is defined. Otherwise, the compute_decomposition() method is used.

The exception to this rule is when the operator is returned from the compute_decomposition() method of another operator, in which case the decomposition is performed with compute_decomposition() (even if this method is defined), and not with this method.

When compute_qfunc_decomposition is defined for an operator, the control flow operations within the method (specifying the decomposition of the operator) are recorded in the JAX representation.

Note

This method is experimental and subject to change.

See also

compute_decomposition().

Parameters:

*args (list) – positional arguments passed to the operator, including trainable parameters and wires
**hyperparameters (dict) – non-trainable hyperparameters of the operator, as stored in the hyperparameters attribute

static compute_sparse_matrix(*params, format='csr', **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
format (str) – format of the returned scipy sparse matrix, for example ‘csr’
**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()¶

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()¶

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.

See also

compute_eigvals() and qml.eigvals()

Returns:: eigenvalues
Return type:: tensor_like

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 LinearCombination and SparseHamiltonian respectively).

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

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. This method is used to simplify Pow instances created by pow() or op ** power.

Operator.pow can be optionally defined by Operator developers, while pow() or op ** power are the entry point for constructing generic powers to exponents.

Parameters:: z (float) – exponent for the operator
Returns:: list[Operator]

>>> class MyClass(qml.operation.Operator):
...
...     def pow(self, z):
...         return [MyClass(self.data[0]*z, self.wires)]
...
>>> op = MyClass(0.5, 0) ** 2
>>> op
MyClass(0.5, wires=[0])**2
>>> op.decomposition()
[MyClass(1.0, wires=[0])]
>>> op.simplify()
MyClass(1.0, wires=[0])

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

simplify()¶

Reduce the depth of nested operators to the minimum.

Returns:: simplified operator
Return type:: .Operator

single_qubit_rot_angles()¶

The parameters required to implement a single-qubit gate as an equivalent Rot gate, up to a global phase.

Returns:: A list of values \([\phi, \theta, \omega]\) such that \(RZ(\omega) RY(\theta) RZ(\phi)\) is equivalent to the original operation.
Return type:: tuple[float, float, float]

sparse_matrix(wire_order=None, format='csr')¶

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
format (str) – format of the returned scipy sparse matrix, for example ‘csr’

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]]

code/api/pennylane.QSVT

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qml.QSVT¶

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