<filename>mrmustard/lab/detectors.py<gh_stars>0
# Copyright 2021 Xanadu Quantum Technologies Inc.

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

#     http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


from mrmustard.types import *
from mrmustard.utils.parametrized import Parametrized
from mrmustard.lab.abstract import State, FockMeasurement
from mrmustard.physics import fock, gaussian
from mrmustard.lab.states import DisplacedSqueezed, Coherent
from mrmustard import settings
from mrmustard.math import Math

math = Math()

__all__ = ["PNRDetector", "ThresholdDetector", "Homodyne", "Heterodyne"]


class PNRDetector(Parametrized, FockMeasurement):
    r"""Photon Number Resolving detector.

    If ``len(modes) > 1`` the detector is applied in parallel to all of the modes provided.
    If a parameter is a single float, the parallel instances of the detector share that parameter.

    To apply mode-specific parmeters use a list of floats. The number of modes is determined (in order of priority)
    by the modes parameter, the cutoffs parameter, or the length of the efficiency and dark counts parameters.

    One can optionally set bounds for each parameter, which the optimizer will respect.

    It can be supplied the full stochastic channel, or it will compute it from
    the quantum efficiency (binomial) and the dark count probability (possonian).

    Args:
        efficiency (float or List[float]): list of quantum efficiencies for each detector
        efficiency_trainable (bool): whether the efficiency is trainable
        efficiency_bounds (Tuple[float, float]): bounds for the efficiency
        dark_counts (float or List[float]): list of expected dark counts
        dark_counts_trainable (bool): whether the dark counts are trainable
        dark_counts_bounds (Tuple[float, float]): bounds for the dark counts
        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
        cutoffs (int or List[int]): largest phton number measurement cutoff for each mode
    """

    def __init__(
        self,
        efficiency: Union[float, List[float]] = 1.0,
        dark_counts: Union[float, List[float]] = 0.0,
        efficiency_trainable: bool = False,
        dark_counts_trainable: bool = False,
        efficiency_bounds: Tuple[Optional[float], Optional[float]] = (0.0, 1.0),
        dark_counts_bounds: Tuple[Optional[float], Optional[float]] = (0.0, None),
        stochastic_channel: Matrix = None,
        modes: List[int] = None,
        cutoffs: Union[int, List[int]] = None,
    ):
        if modes is not None:
            num_modes = len(modes)
        elif cutoffs is not None:
            num_modes = len(cutoffs)
        else:
            num_modes = max(len(math.atleast_1d(efficiency)), len(math.atleast_1d(dark_counts)))

        if len(math.atleast_1d(efficiency)) == 1 and num_modes > 1:
            efficiency = math.tile(math.atleast_1d(efficiency), [num_modes])
        if len(math.atleast_1d(dark_counts)) == 1 and num_modes > 1:
            dark_counts = math.tile(math.atleast_1d(dark_counts), [num_modes])
        Parametrized.__init__(
            self,
            efficiency=efficiency,
            dark_counts=dark_counts,
            efficiency_trainable=efficiency_trainable,
            dark_counts_trainable=dark_counts_trainable,
            efficiency_bounds=efficiency_bounds,
            dark_counts_bounds=dark_counts_bounds,
            stochastic_channel=stochastic_channel,
            modes=modes if modes is not None else list(range(num_modes)),
            cutoffs=cutoffs if cutoffs is not None else [settings.PNR_INTERNAL_CUTOFF] * num_modes,
        )

        self.recompute_stochastic_channel()

    def should_recompute_stochastic_channel(self):
        return self._efficiency_trainable or self._dark_counts_trainable

    def recompute_stochastic_channel(self, cutoffs: List[int] = None):
        if cutoffs is None:
            cutoffs = [settings.PNR_INTERNAL_CUTOFF] * len(self._modes)
        self._internal_stochastic_channel = []
        if self._stochastic_channel is not None:
            self._internal_stochastic_channel = self._stochastic_channel
        else:
            for c, qe, dc in zip(
                cutoffs, math.atleast_1d(self.efficiency)[:], math.atleast_1d(self.dark_counts)[:]
            ):
                dark_prior = math.poisson(max_k=settings.PNR_INTERNAL_CUTOFF, rate=dc)
                condprob = math.binomial_conditional_prob(
                    success_prob=qe, dim_in=settings.PNR_INTERNAL_CUTOFF, dim_out=c
                )
                self._internal_stochastic_channel.append(
                    math.convolve_probs_1d(
                        condprob, [dark_prior, math.eye(settings.PNR_INTERNAL_CUTOFF)[0]]
                    )
                )


class ThresholdDetector(Parametrized, FockMeasurement):
    r"""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).

    Args:
        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
    """

    def __init__(
        self,
        efficiency: Union[float, List[float]] = 1.0,
        dark_count_prob: Union[float, List[float]] = 0.0,
        efficiency_trainable: bool = False,
        dark_count_prob_trainable: bool = False,
        efficiency_bounds: Tuple[Optional[float], Optional[float]] = (0.0, 1.0),
        dark_count_prob_bounds: Tuple[Optional[float], Optional[float]] = (0.0, None),
        stochastic_channel=None,
        modes: List[int] = None,
    ):
        if modes is not None:
            num_modes = len(modes)
        else:
            num_modes = max(len(math.atleast_1d(efficiency)), len(math.atleast_1d(dark_count_prob)))
        if len(math.atleast_1d(efficiency)) == 1 and num_modes > 1:
            efficiency = math.tile(math.atleast_1d(efficiency), [num_modes])
        if len(math.atleast_1d(dark_count_prob)) == 1 and num_modes > 1:
            dark_count_prob = math.tile(math.atleast_1d(dark_count_prob), [num_modes])
        Parametrized.__init__(
            self,
            efficiency=efficiency,
            dark_count_prob=dark_count_prob,
            efficiency_trainable=efficiency_trainable,
            dark_count_prob_trainable=dark_count_prob_trainable,
            efficiency_bounds=efficiency_bounds,
            dark_count_prob_bounds=dark_count_prob_bounds,
            stochastic_channel=stochastic_channel,
            modes=modes or list(range(num_modes)),
            cutoffs=[2] * num_modes,
        )

        self.recompute_stochastic_channel()

    def should_recompute_stochastic_channel(self):
        return self._efficiency_trainable or self._dark_count_prob_trainable

    def recompute_stochastic_channel(self, cutoffs: List[int] = None):
        if cutoffs is None:
            cutoffs = [settings.PNR_INTERNAL_CUTOFF] * len(self._modes)
        self._internal_stochastic_channel = []
        if self._stochastic_channel is not None:
            self._internal_stochastic_channel = self._stochastic_channel
        else:
            for cut, qe, dc in zip(
                cutoffs,
                math.atleast_1d(self.efficiency)[:],
                math.atleast_1d(self.dark_count_prob)[:],
            ):
                row1 = math.pow(1.0 - qe, math.arange(cut)[None, :]) - math.cast(
                    dc, self.efficiency.dtype
                )
                row2 = 1.0 - row1
                # rest = math.zeros((cut - 2, cut), dtype=row1.dtype)
                condprob = math.concat([row1, row2], axis=0)
                self._internal_stochastic_channel.append(condprob)


class Heterodyne(Parametrized, State):
    r"""Heterodyne measurement on given modes."""

    def __new__(
        cls,
        x: Union[float, List[float]] = 0.0,
        y: Union[float, List[float]] = 0.0,
        modes: List[int] = None,
    ):
        instance = Coherent(x=x, y=y, modes=modes)
        instance.__class__ = cls  # NOTE: naughty?
        return instance

    def __init__(self, *args, **kwargs):
        pass


class Homodyne(Parametrized, State):
    r"""Heterodyne measurement on given modes."""

    def __new__(
        cls,
        quadrature_angle: Union[float, List[float]],
        result: Union[float, List[float]] = 0.0,
        modes: List[int] = None,
        r: Union[float, List[float]] = None,
    ):
        quadrature_angle = math.astensor(quadrature_angle, dtype="float64")
        result1 = math.astensor(result, dtype="float64")
        x = result * math.cos(quadrature_angle)
        y = result * math.sin(quadrature_angle)
        instance = DisplacedSqueezed(
            r=settings.HOMODYNE_SQUEEZING if r is None else math.astensor(r, dtype="float64"),
            phi=2 * quadrature_angle,
            x=x,
            y=y,
        )
        instance.__class__ = cls
        return instance

    def __init__(self, *args, **kwargs):
        pass

<filename>mrmustard/lab/detectors.py<gh_stars>0
# Copyright 2021 Xanadu Quantum Technologies Inc.

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

#     http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


from mrmustard.types import *
from mrmustard.utils.parametrized import Parametrized
from mrmustard.lab.abstract import State, FockMeasurement
from mrmustard.physics import fock, gaussian
from mrmustard.lab.states import DisplacedSqueezed, Coherent
from mrmustard import settings
from mrmustard.math import Math

math = Math()

__all__ = ["PNRDetector", "ThresholdDetector", "Homodyne", "Heterodyne"]


class PNRDetector(Parametrized, FockMeasurement):
    r"""Photon Number Resolving detector.

    If ``len(modes) > 1`` the detector is applied in parallel to all of the modes provided.
    If a parameter is a single float, the parallel instances of the detector share that parameter.

    To apply mode-specific parmeters use a list of floats. The number of modes is determined (in order of priority)
    by the modes parameter, the cutoffs parameter, or the length of the efficiency and dark counts parameters.

    One can optionally set bounds for each parameter, which the optimizer will respect.

    It can be supplied the full stochastic channel, or it will compute it from
    the quantum efficiency (binomial) and the dark count probability (possonian).

    Args:
        efficiency (float or List[float]): list of quantum efficiencies for each detector
        efficiency_trainable (bool): whether the efficiency is trainable
        efficiency_bounds (Tuple[float, float]): bounds for the efficiency
        dark_counts (float or List[float]): list of expected dark counts
        dark_counts_trainable (bool): whether the dark counts are trainable
        dark_counts_bounds (Tuple[float, float]): bounds for the dark counts
        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
        cutoffs (int or List[int]): largest phton number measurement cutoff for each mode
    """

    def __init__(
        self,
        efficiency: Union[float, List[float]] = 1.0,
        dark_counts: Union[float, List[float]] = 0.0,
        efficiency_trainable: bool = False,
        dark_counts_trainable: bool = False,
        efficiency_bounds: Tuple[Optional[float], Optional[float]] = (0.0, 1.0),
        dark_counts_bounds: Tuple[Optional[float], Optional[float]] = (0.0, None),
        stochastic_channel: Matrix = None,
        modes: List[int] = None,
        cutoffs: Union[int, List[int]] = None,
    ):
        if modes is not None:
            num_modes = len(modes)
        elif cutoffs is not None:
            num_modes = len(cutoffs)
        else:
            num_modes = max(len(math.atleast_1d(efficiency)), len(math.atleast_1d(dark_counts)))

        if len(math.atleast_1d(efficiency)) == 1 and num_modes > 1:
            efficiency = math.tile(math.atleast_1d(efficiency), [num_modes])
        if len(math.atleast_1d(dark_counts)) == 1 and num_modes > 1:
            dark_counts = math.tile(math.atleast_1d(dark_counts), [num_modes])
        Parametrized.__init__(
            self,
            efficiency=efficiency,
            dark_counts=dark_counts,
            efficiency_trainable=efficiency_trainable,
            dark_counts_trainable=dark_counts_trainable,
            efficiency_bounds=efficiency_bounds,
            dark_counts_bounds=dark_counts_bounds,
            stochastic_channel=stochastic_channel,
            modes=modes if modes is not None else list(range(num_modes)),
            cutoffs=cutoffs if cutoffs is not None else [settings.PNR_INTERNAL_CUTOFF] * num_modes,
        )

        self.recompute_stochastic_channel()

    def should_recompute_stochastic_channel(self):
        return self._efficiency_trainable or self._dark_counts_trainable

    def recompute_stochastic_channel(self, cutoffs: List[int] = None):
        if cutoffs is None:
            cutoffs = [settings.PNR_INTERNAL_CUTOFF] * len(self._modes)
        self._internal_stochastic_channel = []
        if self._stochastic_channel is not None:
            self._internal_stochastic_channel = self._stochastic_channel
        else:
            for c, qe, dc in zip(
                cutoffs, math.atleast_1d(self.efficiency)[:], math.atleast_1d(self.dark_counts)[:]
            ):
                dark_prior = math.poisson(max_k=settings.PNR_INTERNAL_CUTOFF, rate=dc)
                condprob = math.binomial_conditional_prob(
                    success_prob=qe, dim_in=settings.PNR_INTERNAL_CUTOFF, dim_out=c
                )
                self._internal_stochastic_channel.append(
                    math.convolve_probs_1d(
                        condprob, [dark_prior, math.eye(settings.PNR_INTERNAL_CUTOFF)[0]]
                    )
                )


class ThresholdDetector(Parametrized, FockMeasurement):
    r"""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).

    Args:
        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
    """

    def __init__(
        self,
        efficiency: Union[float, List[float]] = 1.0,
        dark_count_prob: Union[float, List[float]] = 0.0,
        efficiency_trainable: bool = False,
        dark_count_prob_trainable: bool = False,
        efficiency_bounds: Tuple[Optional[float], Optional[float]] = (0.0, 1.0),
        dark_count_prob_bounds: Tuple[Optional[float], Optional[float]] = (0.0, None),
        stochastic_channel=None,
        modes: List[int] = None,
    ):
        if modes is not None:
            num_modes = len(modes)
        else:
            num_modes = max(len(math.atleast_1d(efficiency)), len(math.atleast_1d(dark_count_prob)))
        if len(math.atleast_1d(efficiency)) == 1 and num_modes > 1:
            efficiency = math.tile(math.atleast_1d(efficiency), [num_modes])
        if len(math.atleast_1d(dark_count_prob)) == 1 and num_modes > 1:
            dark_count_prob = math.tile(math.atleast_1d(dark_count_prob), [num_modes])
        Parametrized.__init__(
            self,
            efficiency=efficiency,
            dark_count_prob=dark_count_prob,
            efficiency_trainable=efficiency_trainable,
            dark_count_prob_trainable=dark_count_prob_trainable,
            efficiency_bounds=efficiency_bounds,
            dark_count_prob_bounds=dark_count_prob_bounds,
            stochastic_channel=stochastic_channel,
            modes=modes or list(range(num_modes)),
            cutoffs=[2] * num_modes,
        )

        self.recompute_stochastic_channel()

    def should_recompute_stochastic_channel(self):
        return self._efficiency_trainable or self._dark_count_prob_trainable

    def recompute_stochastic_channel(self, cutoffs: List[int] = None):
        if cutoffs is None:
            cutoffs = [settings.PNR_INTERNAL_CUTOFF] * len(self._modes)
        self._internal_stochastic_channel = []
        if self._stochastic_channel is not None:
            self._internal_stochastic_channel = self._stochastic_channel
        else:
            for cut, qe, dc in zip(
                cutoffs,
                math.atleast_1d(self.efficiency)[:],
                math.atleast_1d(self.dark_count_prob)[:],
            ):
                row1 = math.pow(1.0 - qe, math.arange(cut)[None, :]) - math.cast(
                    dc, self.efficiency.dtype
                )
                row2 = 1.0 - row1
                # rest = math.zeros((cut - 2, cut), dtype=row1.dtype)
                condprob = math.concat([row1, row2], axis=0)
                self._internal_stochastic_channel.append(condprob)


class Heterodyne(Parametrized, State):
    r"""Heterodyne measurement on given modes."""

    def __new__(
        cls,
        x: Union[float, List[float]] = 0.0,
        y: Union[float, List[float]] = 0.0,
        modes: List[int] = None,
    ):
        instance = Coherent(x=x, y=y, modes=modes)
        instance.__class__ = cls  # NOTE: naughty?
        return instance

    def __init__(self, *args, **kwargs):
        pass


class Homodyne(Parametrized, State):
    r"""Heterodyne measurement on given modes."""

    def __new__(
        cls,
        quadrature_angle: Union[float, List[float]],
        result: Union[float, List[float]] = 0.0,
        modes: List[int] = None,
        r: Union[float, List[float]] = None,
    ):
        quadrature_angle = math.astensor(quadrature_angle, dtype="float64")
        result1 = math.astensor(result, dtype="float64")
        x = result * math.cos(quadrature_angle)
        y = result * math.sin(quadrature_angle)
        instance = DisplacedSqueezed(
            r=settings.HOMODYNE_SQUEEZING if r is None else math.astensor(r, dtype="float64"),
            phi=2 * quadrature_angle,
            x=x,
            y=y,
        )
        instance.__class__ = cls
        return instance

    def __init__(self, *args, **kwargs):
        pass

# Copyright 2021 Xanadu Quantum Technologies Inc. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at #     http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. Photon Number Resolving detector.      If ``len(modes) > 1`` the detector is applied in parallel to all of the modes provided.     If a parameter is a single float, the parallel instances of the detector share that parameter.      To apply mode-specific parmeters use a list of floats. The number of modes is determined (in order of priority)     by the modes parameter, the cutoffs parameter, or the length of the efficiency and dark counts parameters.      One can optionally set bounds for each parameter, which the optimizer will respect.      It can be supplied the full stochastic channel, or it will compute it from     the quantum efficiency (binomial) and the dark count probability (possonian).      Args:         efficiency (float or List[float]): list of quantum efficiencies for each detector         efficiency_trainable (bool): whether the efficiency is trainable         efficiency_bounds (Tuple[float, float]): bounds for the efficiency         dark_counts (float or List[float]): list of expected dark counts         dark_counts_trainable (bool): whether the dark counts are trainable         dark_counts_bounds (Tuple[float, float]): bounds for the dark counts         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         cutoffs (int or List[int]): largest phton number measurement cutoff for each mode 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).      Args:         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 # rest = math.zeros((cut - 2, cut), dtype=row1.dtype) Heterodyne measurement on given modes. # NOTE: naughty? Heterodyne measurement on given modes.