qml.ftqc.ParametricMidMeasureMP¶

class ParametricMidMeasureMP(wires, *, angle, plane, reset=False, postselect=None, id=None)[source]¶

Bases: MidMeasureMP

Parametric mid-circuit measurement. The basis for the measurement is parametrized by a plane (“XY”, “YZ” or “ZX”), and an angle within the plane.

This class additionally stores information about unknown measurement outcomes in the qubit model. Measurements on a single qubit are assumed.

Warning

Measurements should be diagonalized before execution for any device that only natively supports mid-circuit measurements in the computational basis. To diagonalize, the diagonalize_mcms transform can be applied.

Skipping diagonalization for a circuit containing parametric mid-circuit measurements may result in a completed execution with incorrect results.

Parameters:

wires (.Wires) – The wires the measurement process applies to. This can only be specified if an observable was not provided.

Keyword Arguments:

angle (float) – The angle in radians
plane (str) – The plane the measurement basis lies in. Options are “XY”, “ZX” and “YZ”
reset (bool) – Whether to reset the wire after measurement.
postselect (Optional[int]) – Which basis state to postselect after a mid-circuit measurement. None by default. If postselection is requested, only the post-measurement state that is used for postselection will be considered in the remaining circuit.
id (str) – Custom label given to a measurement instance.

Attributes

`angle`	The angle in radians
`arithmetic_depth`	Arithmetic depth of the operator.
`batch_size`
`has_adjoint`
`has_decomposition`
`has_diagonalizing_gates`
`has_generator`
`has_matrix`
`has_qfunc_decomposition`
`has_sparse_matrix`
`hash`	Returns an integer hash uniquely representing the measurement process
`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 likely hermitian.
`name`	String for the name of the operator.
`ndim_params`	Number of dimensions per trainable parameter of the operator.
`num_params`
`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.
`plane`	The plane the measurement basis lies in.
`postselect`	Which basis state to postselect after a mid-circuit measurement.
`reset`	Whether to reset the wire into the zero state after the measurement.
`resource_keys`
`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.

angle¶: The angle in radians

arithmetic_depth¶: Arithmetic depth of the operator.

batch_size = None¶

has_adjoint = False¶

has_decomposition = False¶

has_diagonalizing_gates = True¶

has_generator = False¶

has_matrix = False¶

has_qfunc_decomposition = False¶

has_sparse_matrix = False¶

hash¶

Returns an integer hash uniquely representing the measurement process

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 likely hermitian.

Note

It is recommended to use the is_hermitian() function. Although this function may be expensive to calculate, the op.is_hermitian property can lead to technically incorrect results.

If this property returns True, the operator is guaranteed to be hermitian, but if it returns False, the operator may still be hermitian.

As an example, consider the following edge case:

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

On the contrary, the is_hermitian() function will give the correct answer:

>>> qml.is_hermitian(op)
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 = 0¶

num_wires = 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.

plane¶: The plane the measurement basis lies in. Options are “XY”, “ZX” and “YZ

postselect¶: Which basis state to postselect after a mid-circuit measurement.

reset¶: Whether to reset the wire into the zero state after the measurement.

resource_keys = {}¶

resource_params¶

A dictionary containing the minimal information needed to compute a resource estimate of the operator’s decomposition.

The keys of this dictionary should match the resource_keys attribute of the operator class. Two instances of the same operator type should have identical resource_params iff their decompositions exhibit the same counts for each gate type, even if the individual gate parameters differ.

Examples

The MultiRZ has non-empty resource_keys:

>>> qml.MultiRZ.resource_keys
{'num_wires'}

The resource_params of an instance of MultiRZ will contain the number of wires:

>>> op = qml.MultiRZ(0.5, wires=[0, 1])
>>> op.resource_params
{'num_wires': 2}

Note that another MultiRZ may have different parameters but the same resource_params:

>>> op2 = qml.MultiRZ(0.7, wires=[1, 2])
>>> op2.resource_params
{'num_wires': 2}

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`(*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_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`()	Decompose to a diagonalizing gate and a standard MCM 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])	How the mid-circuit measurement is represented in diagrams and drawings.
`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()¶

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(*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_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()[source]¶: Decompose to a diagonalizing gate and a standard MCM in the computational basis

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]¶

How the mid-circuit measurement is represented in diagrams and drawings.

Parameters:

decimals – If None, no parameters are included. Else, how to round the parameters. Defaults to None.
base_label – overwrite the non-parameter component of the label. Required to match general call signature. Not used.
cache – dictionary that carries information between label calls in the same drawing. Required to match general call signature. Not used.

Returns:

label to use in drawings

Return type:

str

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. 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'>)¶: 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, 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]]

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qml.ftqc.ParametricMidMeasureMP¶

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