qml.measurements.MeasurementTransform¶

class MeasurementTransform(obs=None, wires=None, eigvals=None, id=None)[source]¶

Bases: MeasurementProcess

Measurement process that applies a transform into the given quantum tape. This transform is carried out inside the gradient black box, thus is not tracked by the gradient transform.

Any class inheriting from MeasurementTransform should define its own process method, which should have the following arguments:

tape (QuantumTape): quantum tape to transform
device (pennylane.devices.LegacyDevice): device used to transform the quantum tape

Attributes

`has_decomposition`	Whether or not the MeasurementProcess returns a defined decomposition when calling `expand`.
`hash`	returns an integer hash uniquely representing the measurement process
`numeric_type`	The Python numeric type of the measurement result.
`raw_wires`	The wires the measurement process acts on.
`samples_computational_basis`	Whether or not the MeasurementProcess measures in the computational basis.
`wires`	The wires the measurement process acts on.

has_decomposition¶

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

Type:: Bool

hash¶

returns an integer hash uniquely representing the measurement process

Type:: int

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()

samples_computational_basis¶

Whether or not the MeasurementProcess measures in the computational basis.

Type:: Bool

wires¶

The wires the measurement process acts on.

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

Methods

`diagonalizing_gates`()	Returns the gates that diagonalize the measured wires such that they are in the eigenbasis of the circuit observables.
`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.
`map_wires`(wire_map)	Returns a copy of the current measurement process with its wires changed according to the given wire map.
`process`(tape, device)	Process the given quantum tape.
`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()¶

Returns the gates that diagonalize the measured wires such that they are in the eigenbasis of the circuit observables.

Returns:: the operations that diagonalize the observables
Return type:: List[.Operation]

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

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

abstract process(tape, device)[source]¶

Process the given quantum tape.

Parameters:

tape (QuantumTape) – quantum tape to transform
device (pennylane.devices.LegacyDevice) – device used to transform the quantum tape

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

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qml.measurements.MeasurementTransform¶

Attributes

Methods

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