mm.lab.ThresholdDetector

class mrmustard.lab.ThresholdDetector(efficiency=1.0, dark_count_prob=0.0, efficiency_trainable=False, dark_count_prob_trainable=False, efficiency_bounds=(0.0, 1.0), dark_count_prob_bounds=(0.0, None), stochastic_channel=None, modes=None)

Bases: FockMeasurement

Threshold detector: any Fock component other than vacuum counts toward a click in the detector.

If len(modes) > 1 the detector is applied in parallel to all of the modes provided.

If a parameter is a single float, its value is applied to all of the parallel instances of the detector.

To apply mode-specific values use a list of floats.

It can be supplied the full conditional detection probabilities, or it will compute them from the quantum efficiency (binomial) and the dark count probability (bernoulli).

Parameters:

• efficiency (float or List[float]) – list of quantum efficiencies for each detector

• dark_count_prob (float or List[float]) – list of dark count probabilities for each detector

• efficiency_trainable (bool) – whether the efficiency is trainable

• dark_count_prob_trainable (bool) – whether the dark count probabilities are trainable

• efficiency_bounds (Tuple[float, float]) – bounds for the efficiency

• dark_count_prob_bounds (Tuple[float, float]) – bounds for the dark count probabilities

• stochastic_channel (Optional 2d array) – if supplied, this stochastic_channel will be used for belief propagation

• modes (Optional List[int]) – list of modes to apply the detector to

Attributes

modes	returns the modes being measured
num_modes	returns the number of modes being measured
outcome	Returns outcome of the measurement.
parameter_set	The set of parameters for this transformation.
postselected	returns whether the measurement is postselected, i.e, a outcome has been provided

modes

returns the modes being measured

num_modes

returns the number of modes being measured

outcome

parameter_set

The set of parameters for this transformation.

postselected

returns whether the measurement is postselected, i.e, a outcome has been provided

Methods

primal(other)	performs the measurement procedure according to the representation of the incoming state
recompute_stochastic_channel([cutoffs])	recompute belief using the defined stochastic channel
should_recompute_stochastic_channel()	Returns True if the stochastic channel has to be recomputed.

primal(other)

performs the measurement procedure according to the representation of the incoming state

Return type:

Union[State, float]

recompute_stochastic_channel(cutoffs=None)

recompute belief using the defined stochastic channel

should_recompute_stochastic_channel()

Returns True if the stochastic channel has to be recomputed.

This method should be overriden by subclasses as needed.

code/api/mrmustard.lab.ThresholdDetector

 

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