qml.measurements.ShadowExpvalMP¶

class
ShadowExpvalMP
(H, seed=None, k=1, id=None)[source]¶ Bases:
pennylane.measurements.measurements.MeasurementTransform
Measures the expectation value of an operator using the classical shadow measurement process.
Please refer to
shadow_expval()
for detailed documentation. Parameters
H (Operator, Sequence[Operator]) – Operator or list of Operators to compute the expectation value over.
seed (Union[int, None]) – The seed used to generate the random measurements
k (int) – Number of equal parts to split the shadow’s measurements to compute the median of means.
k=1
corresponds to simply taking the mean over all measurements.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
¶

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
(tape, device)Process the given quantum tape.
process_state_with_shots
(state, wire_order, …)Process the given quantum state with the given number of shots
queue
([context])Append the measurement process to an annotated queue, making sure the observable is not queued
shape
(device, shots)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
(tape, device)[source]¶ Process the given quantum tape.
 Parameters
tape (QuantumTape) – quantum tape to transform
device (pennylane.Device) – device used to transform the quantum tape

process_state_with_shots
(state, wire_order, shots, rng=None)[source]¶ Process the given quantum state with the given number of shots
 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 \(n\) wiresshots (int) – The number of shots
rng (Union[None, int, array_like[int], SeedSequence, BitGenerator, Generator]) – A seedlike parameter matching that of
seed
fornumpy.random.default_rng
. If no value is provided, a default RNG will be used.
 Returns
The estimate of the expectation value.
 Return type
float

queue
(context=<class 'pennylane.queuing.QueuingManager'>)[source]¶ Append the measurement process to an annotated queue, making sure the observable is not queued

shape
(device, shots)[source]¶ The expected output shape of the MeasurementProcess.
Note that the output shape is dependent on the shots or device when:
The measurement type is either
_Probability
,_State
(fromstate()
) or_Sample
;The shot vector was defined.
For example, assuming a device with
shots=None
, expectation values and variances defineshape=(,)
, whereas probabilities in the qubit model defineshape=(2**num_wires)
wherenum_wires
is the number of wires the measurement acts on. Parameters
device (pennylane.Device) – a PennyLane device to use for determining the shape
shots (Shots) – object defining the number and batches of shots
 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