qml.measurements.StateMP¶

class
StateMP
(wires=None, id=None)[source]¶ Bases:
pennylane.measurements.measurements.StateMeasurement
Measurement process that returns the quantum state in the computational basis.
Please refer to
pennylane.state()
for detailed documentation. Parameters
wires (Wires) – The wires the measurement process applies to.
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.
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
= 'state'¶

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_density_matrix
(density_matrix, …)Process the given density matrix.
process_state
(state, wire_order)Process the given quantum state.
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
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_density_matrix
(density_matrix, wire_order)[source]¶ Process the given density matrix.
 Parameters
density_matrix (TensorLike) – The density matrix representing the (mixed) quantum state, which may be single or batched. For a single matrix, the shape should be
(2^n, 2^n)
where n is the number of wires the matrix acts upon. For batched matrices, the shape should be(batch_size, 2^n, 2^n)
.wire_order (Wires) – The wires determining the subspace that the
density_matrix
acts on. A matrix of dimension \(2^n\) acts on a subspace of \(n\) wires. This parameter specifies the mapping of matrix dimensions to physical qubits, allowing the function to correctly trace out the subsystems not involved in the measurement or operation.

process_state
(state, wire_order)[source]¶ Process the given quantum state.
 Parameters
state (Sequence[complex]) – quantum state with a flat shape. It may also have an optional batch dimension
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
(shots=None, num_device_wires=0)[source]¶ 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