qml.ftqc.YMidMeasureMP

class YMidMeasureMP(wires, reset=False, postselect=None, id=None)

Bases: pennylane.ftqc.parametric_midmeasure.ParametricMidMeasureMP

A subclass of ParametricMidMeasureMP that uses the Y measurement basis (angle=pi/2, plane=”XY”). For labels and visualizations, this will be represented as a Y measurement. It is otherwise identical to the parent class.

Attributes

data	The data of the measurement.
has_decomposition	Whether or not the MeasurementProcess returns a defined decomposition when calling expand.
has_diagonalizing_gates	Whether there are gates that need to be applied to diagonalize the measurement
hash	Returns an integer hash uniquely representing the measurement process
name	The name of the measurement.
num_params	The number of parameters.
numeric_type	The Python numeric type of the measurement result.
raw_wires	The wires the measurement process acts on.
return_type	Measurement return type.
samples_computational_basis	Whether or not the MeasurementProcess measures in the computational basis.
wires	The wires the measurement process acts on.

data

The data of the measurement. Needed to match the Operator API.

has_decomposition

Whether or not the MeasurementProcess returns a defined decomposition when calling expand.

Type

Bool

has_diagonalizing_gates

Whether there are gates that need to be applied to diagonalize the measurement

hash

Returns an integer hash uniquely representing the measurement process

Type

int

name

The name of the measurement. Needed to match the Operator API.

num_params

The number of parameters. Needed to match the Operator API.

numeric_type

The Python numeric type of the measurement result.

Returns

The output numeric type; int, float or complex.

Return type

type

Raises

QuantumFunctionError – the return type of the measurement process is unrecognized and cannot deduce the numeric type

raw_wires

The wires the measurement process acts on.

For measurements involving more than one set of wires (such as mutual information), this is a list of the Wires objects. Otherwise, this is the same as wires()

return_type

Measurement return type.

samples_computational_basis

wires

The wires the measurement process acts on.

This is the union of all the Wires objects of the measurement.

Methods

diagonalizing_gates()	Decompose to a diagonalizing gate and a standard MCM in the computational basis
eigvals()	Eigenvalues associated with the measurement process.
expand()	Expand the measurement of an observable to a unitary rotation and a measurement in the computational basis.
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 measurement process with its wires changed according to the given wire map.
queue([context])	Append the measurement process to an annotated queue.
shape([shots, num_device_wires])	Calculate the shape of the result object tensor.
simplify()	Reduce the depth of the observable to the minimum.

diagonalizing_gates()

Decompose to a diagonalizing gate and a standard MCM in the computational basis

eigvals()

Eigenvalues associated with the measurement process.

If the measurement process has an associated observable, the eigenvalues will correspond to this observable. Otherwise, they will be the eigenvalues provided when the measurement process was instantiated.

Note that the eigenvalues are not guaranteed to be in any particular order.

Example:

>>> m = MeasurementProcess(Expectation, obs=qml.X(1))
>>> m.eigvals()
array([1, -1])

Returns

eigvals representation

Return type

array

expand()

Expand the measurement of an observable to a unitary rotation and a measurement in the computational basis.

Returns

a quantum tape containing the operations required to diagonalize the observable

Return type

QuantumTape

Example:

Consider a measurement process consisting of the expectation value of an Hermitian observable:

>>> H = np.array([[1, 2], [2, 4]])
>>> obs = qml.Hermitian(H, wires=['a'])
>>> m = MeasurementProcess(Expectation, obs=obs)

Expanding this out:

>>> tape = m.expand()

We can see that the resulting tape has the qubit unitary applied, and a measurement process with no observable, but the eigenvalues specified:

>>> print(tape.operations)
[QubitUnitary(array([[-0.89442719,  0.4472136 ],
      [ 0.4472136 ,  0.89442719]]), wires=['a'])]
>>> print(tape.measurements[0].eigvals())
[0. 5.]
>>> print(tape.measurements[0].obs)
None

label(decimals=None, base_label=None, cache=None)

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. Required to match general call signature. Not used.

• 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 measurement process 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 measurement process

Return type

MeasurementProcess

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

Append the measurement process to an annotated queue.

shape(shots=None, num_device_wires=0)

Calculate the shape of the result object tensor.

Parameters

• shots (Optional[int]) – the number of shots used execute the circuit. None indicates an analytic simulation. Shot vectors are handled by calling this method multiple times.

• num_device_wires (int) – The number of wires that will be used if the measurement is broadcasted across all available wires (len(mp.wires) == 0). If the device itself doesn’t provide a number of wires, the number of tape wires will be provided here instead:

Returns

An arbitrary length tuple of ints. May be an empty tuple.

Return type

tuple[int,…]

>>> qml.probs(wires=(0,1)).shape()
(4,)
>>> qml.sample(wires=(0,1)).shape(shots=50)
(50, 2)
>>> qml.state().shape(num_device_wires=4)
(16,)
>>> qml.expval(qml.Z(0)).shape()
()

simplify()

Reduce the depth of the observable to the minimum.

Returns

A measurement process with a simplified observable.

Return type

MeasurementProcess

code/api/pennylane.ftqc.YMidMeasureMP

 

Download Python script

 

Download Notebook

 

View on GitHub