qml.measurements.VnEntropyMP¶

class
VnEntropyMP
(obs=None, wires=None, eigvals=None, id=None, log_base=None)[source]¶ Bases:
pennylane.measurements.measurements.StateMeasurement
Measurement process that computes the Von Neumann entropy of the system prior to measurement.
Please refer to
vn_entropy()
for detailed documentation. Parameters
obs (Observable) – The observable that is to be measured as part of the measurement process. Not all measurement processes require observables (for example
Probability
); this argument is optional.wires (Wires) – The wires the measurement process applies to. This can only be specified if an observable was not provided.
eigvals (array) – A flat array representing the eigenvalues of the measurement. This can only be specified if an observable was not provided.
id (str) – custom label given to a measurement instance, can be useful for some applications where the instance has to be identified
Attributes
Whether or not the MeasurementProcess returns a defined decomposition when calling
expand
.returns an integer hash uniquely representing the measurement process
The Python numeric type of the measurement result.
The wires the measurement process acts on.
Measurement return type.
Whether or not the MeasurementProcess measures in the computational basis.
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
¶

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
¶

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
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_state
(state, wire_order)Process the given quantum state.
queue
([context])Append the measurement process to an annotated queue.
shape
([device])The expected output shape of the MeasurementProcess.
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.PauliX(wires=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
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

process_state
(state, wire_order)[source]¶ Process the given quantum state.
 Parameters
state (Sequence[complex]) – quantum state
wire_order (Wires) – wires determining the subspace that
state
acts on; a matrix of dimension \(2^n\) acts on a subspace of \(n\) wires

queue
(context=<class 'pennylane.queuing.QueuingManager'>)¶ Append the measurement process to an annotated queue.

shape
(device=None)[source]¶ The expected output shape of the MeasurementProcess.
Note that the output shape is dependent on the device when:
The measurement type is either
ProbabilityMP
,StateMP
(fromstate()
) orSampleMP
;The shot vector was defined in the device.
For example, assuming a device with
shots=None
, expectation values and variances defineshape=(1,)
, whereas probabilities in the qubit model defineshape=(1, 2**num_wires)
wherenum_wires
is the number of wires the measurement acts on.Note that the shapes for vectorvalued measurements such as
ProbabilityMP
andStateMP
are adjusted to the output ofqml.execute
and may have an extra first element that is squeezed when using QNodes. Parameters
device (pennylane.Device) – a PennyLane device to use for determining the shape
 Returns
the output shape
 Return type
tuple
 Raises
QuantumFunctionError – the return type of the measurement process is unrecognized and cannot deduce the numeric type

simplify
()¶ Reduce the depth of the observable to the minimum.
 Returns
A measurement process with a simplified observable.
 Return type