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

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.

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.

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.

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.

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

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

process(tape, device)[source]

Process the given quantum tape.

Parameters
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 \(n\) wires

  • shots (int) – The number of shots

  • rng (Union[None, int, array_like[int], SeedSequence, BitGenerator, Generator]) – A seed-like parameter matching that of seed for numpy.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 (from state()) or _Sample;

  • The shot vector was defined.

For example, assuming a device with shots=None, expectation values and variances define shape=(,), whereas probabilities in the qubit model define shape=(2**num_wires) where num_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

MeasurementProcess