merlin.measurement.process module

Quantum measurement sampling utilities.

class merlin.measurement.process.Callable

Bases: object

class merlin.measurement.process.PartialMeasurement(branches, measured_modes, unmeasured_modes, grouping=None)

Bases: object

Collection of measurement branches along with measured/unmeasured mode metadata.

Args:

branches: Tuple of branches, ordered lexicographically by outcome. measured_modes: Indices of measured modes in the full system. unmeasured_modes: Indices of unmeasured modes in the full system. grouping: Optional grouping callable to group probabilities.

property amplitudes

static from_detector_transform_output(detector_output, *, grouping=None)

Branch-based PartialMeasurement wrapper from DetectorTransform(partial_measurement=True) output.

Return type:

PartialMeasurement

property n_measured_modes: int

property n_unmeasured_modes: int

property outcomes

property probabilities: Tensor

Return the probabilities of all branches as a tensor of shape (batch, n_branches) unless a grouping was set in which case, the probabilities are grouped and the returned tensor has shape (batch, grouping_output_size).

Same property as self.tensor.

property probability_tensor_shape: tuple[int, int]

Return the expected (batch, n_outcomes) shape for the probability tensor.

reorder_branches()

Reorder branches lexicographically by their outcomes.

Return type:

None

set_grouping(grouping)

Set the grouping used to group probabilities.

Once the grouping is set, the properties probabilities and tensor return grouped probabilities.

Return type:

None

Args:

grouping: Grouping object used to group probabilities.

property tensor: Tensor

Return the probabilities of all branches as a tensor of shape (batch, n_branches). unless a grouping was set in which case, the probabilities are grouped and the returned tensor has shape (batch, grouping_output_size).

This property assumes that all branches are ordered lexicographically by their outcomes so the stacking of probabilities follows the same order.

verify_branches_order()

Verify that branches are ordered lexicographically by their outcomes.

Return type:

None

class merlin.measurement.process.SamplingProcess(method='multinomial')

Bases: object

Handles quantum measurement sampling with different methods.

This class provides functionality to simulate quantum measurement noise by applying different sampling strategies to probability distributions.

pcvl_sampler(distribution, shots, method=None)

Apply sampling noise to a probability distribution.

Return type:

Tensor

Args:

distribution: Input probability distribution tensor method: Sampling method to use (‘multinomial’, ‘binomial’, or ‘gaussian’), defaults to the initialized method shots: Number of measurement shots to simulate

Returns:

Noisy probability distribution after sampling

Raises:

ValueError: If method is not one of the valid options

merlin.measurement.process.partial_measurement(detector_output, *, grouping=None)

Build a PartialMeasurement from DetectorTransform(partial_measurement=True) output.

Return type:

PartialMeasurement