diff --git a/.github/CHANGELOG.md b/.github/CHANGELOG.md index e137e8b9d..c36c1da24 100644 --- a/.github/CHANGELOG.md +++ b/.github/CHANGELOG.md @@ -1,3 +1,50 @@ +# Release 0.6.0 (current release) + +### New features + +* Added a new method to discretize Wigner functions that revolves Clenshaw summations. This method is expected to be fast and +reliable for systems with high number of excitations, for which the pre-existing iterative method is known to be unstable. Users +can select their preferred methods by setting the value of `Settings.DISCRETIZATION_METHOD` to either `interactive` (default) or +`clenshaw`. + [(#280)](https://github.com/XanaduAI/MrMustard/pull/280) + +* Added the `PhaseNoise(phase_stdev)` gate (non-Gaussian). Output is a mixed state in Fock representation. + It is not based on a choi operator, but on a nonlinear transformation of the density matrix. + [(#275)](https://github.com/XanaduAI/MrMustard/pull/275) + +### Breaking changes + +* The value of `hbar` can no longer be specified outside of `Settings`. All the classes and + methods that allowed specifying its value as an input now retrieve it directly from `Settings`. + [(#278)](https://github.com/XanaduAI/MrMustard/pull/278) + +* Certain attributes of `Settings` can no longer be changed after their value is queried for the + first time. + [(#278)](https://github.com/XanaduAI/MrMustard/pull/278) + +### Improvements + +* Tensorflow bumped to v2.14 with poetry installation working out of the box on Linux and Mac. + [(#281)](https://github.com/XanaduAI/MrMustard/pull/281) + +### Bug fixes + +* Fixed a bug about the variable names in functions (apply_kraus_to_ket, apply_kraus_to_dm, apply_choi_to_ket, apply_choi_to_dm). + [(#271)](https://github.com/XanaduAI/MrMustard/pull/271) + +* Fixed a bug that was leading to an error when computing the Choi representation of a unitary transformation. + [(#283)](https://github.com/XanaduAI/MrMustard/pull/283) + +* Fixed the internal function to calculate ABC of Bargmann representation (now corresponds to the literature) and other fixes to get the correct Fock tensor. + [(#255)](https://github.com/XanaduAI/MrMustard/pull/255) + +### Documentation + +### Contributors +[Filippo Miatto](https://github.com/ziofil), [Samuele Ferracin](https://github.com/SamFerracin), [Yuan Yao](https://github.com/sylviemonet), [Zeyue Niu](https://github.com/zeyueN) + +--- + # Release 0.5.0 ### New features @@ -35,7 +82,7 @@ # Or, in command line: `tensorboard --logdir={tb_cb.logdir}` and open link in browser. ``` -* Gaussian states support a `bargmann` method for returning the bargmann representation. +* Gaussian states support a `bargmann` method for returning the bargmann representation. [(#235)](https://github.com/XanaduAI/MrMustard/pull/235) * The `ket` method of `State` now supports new keyword arguments `max_prob` and `max_photons`. @@ -50,7 +97,7 @@ ket = Gaussian(2).ket(max_prob=0.99, max_photons=3) ``` -* Gaussian transformations support a `bargmann` method for returning the bargmann representation. +* Gaussian transformations support a `bargmann` method for returning the bargmann representation. [(#239)](https://github.com/XanaduAI/MrMustard/pull/239) * BSGate.U now supports method='vanilla' (default) and 'schwinger' (slower, but stable to any cutoff) @@ -82,10 +129,11 @@ [(#239)](https://github.com/XanaduAI/MrMustard/pull/239) * More robust implementation of cutoffs for States. -[(#239)](https://github.com/XanaduAI/MrMustard/pull/239) + + [(#239)](https://github.com/XanaduAI/MrMustard/pull/239) * Dependencies and versioning are now managed using Poetry. -[(#257)](https://github.com/XanaduAI/MrMustard/pull/257) + [(#257)](https://github.com/XanaduAI/MrMustard/pull/257) ### Bug fixes diff --git a/mrmustard/__init__.py b/mrmustard/__init__.py index f8ed18e69..5c9afcacb 100644 --- a/mrmustard/__init__.py +++ b/mrmustard/__init__.py @@ -14,89 +14,11 @@ """This is the top-most `__init__.py` file of MrMustard package.""" -import numpy as np -import rich.table + from rich import print from ._version import __version__ - - -# pylint: disable=too-many-instance-attributes -class Settings: - """Settings class.""" - - def __new__(cls): # singleton - if not hasattr(cls, "instance"): - cls.instance = super(Settings, cls).__new__(cls) - return cls.instance - - def __init__(self): - self._backend = "tensorflow" - self.HBAR = 2.0 - self.CHOI_R = 0.881373587019543 # np.arcsinh(1.0) - self.DEBUG = False - self.AUTOCUTOFF_PROBABILITY = 0.999 # capture at least 99.9% of the probability - self.AUTOCUTOFF_MAX_CUTOFF = 100 - self.AUTOCUTOFF_MIN_CUTOFF = 1 - self.CIRCUIT_DECIMALS = 3 - # use cutoff=5 for each mode when determining if two transformations in fock repr are equal - self.EQ_TRANSFORMATION_CUTOFF = 3 # 3 is enough to include a full step of the rec relations - self.EQ_TRANSFORMATION_RTOL_FOCK = 1e-3 - self.EQ_TRANSFORMATION_RTOL_GAUSS = 1e-6 - # for the detectors - self.PNR_INTERNAL_CUTOFF = 50 - self.HOMODYNE_SQUEEZING = 10.0 - # misc - self.PROGRESSBAR = True - self._seed = np.random.randint(0, 2**31 - 1) - self.rng = np.random.default_rng(self._seed) - self.DEFAULT_BS_METHOD = "vanilla" # can be 'vanilla' or 'schwinger' - - @property - def SEED(self): - """Returns the seed value if set, otherwise returns a random seed.""" - if self._seed is None: - self._seed = np.random.randint(0, 2**31 - 1) - self.rng = np.random.default_rng(self._seed) - return self._seed - - @SEED.setter - def SEED(self, value): - """Sets the seed value.""" - self._seed = value - self.rng = np.random.default_rng(self._seed) - - @property - def BACKEND(self): - """The backend which is used. - - Can be either ``'tensorflow'`` or ``'torch'``. - """ - return self._backend - - @BACKEND.setter - def BACKEND(self, backend_name: str): - if backend_name not in ["tensorflow", "torch"]: # pragma: no cover - raise ValueError("Backend must be either 'tensorflow' or 'torch'") - self._backend = backend_name - - # use rich.table to print the settings - def __repr__(self): - """Returns a string representation of the settings.""" - table = rich.table.Table(title="MrMustard Settings") - table.add_column("Setting") - table.add_column("Value") - table.add_row("BACKEND", self.BACKEND) - table.add_row("SEED", str(self.SEED)) - for key, value in self.__dict__.items(): - if key == key.upper(): - table.add_row(key, str(value)) - print(table) - return "" - - -settings = Settings() -"""Settings object.""" +from .settings import * def version(): diff --git a/mrmustard/lab/abstract/state.py b/mrmustard/lab/abstract/state.py index 8eb8c0e01..1ca13207e 100644 --- a/mrmustard/lab/abstract/state.py +++ b/mrmustard/lab/abstract/state.py @@ -94,7 +94,7 @@ def __init__( self._norm = _norm if cov is not None and means is not None: self.is_gaussian = True - self.is_hilbert_vector = np.allclose(gaussian.purity(self.cov, settings.HBAR), 1.0) + self.is_hilbert_vector = np.allclose(gaussian.purity(self.cov), 1.0) self.num_modes = cov.shape[-1] // 2 elif eigenvalues is not None and symplectic is not None: self.is_gaussian = True @@ -140,7 +140,7 @@ def purity(self) -> float: """Returns the purity of the state.""" if self._purity is None: if self.is_gaussian: - self._purity = gaussian.purity(self.cov, settings.HBAR) + self._purity = gaussian.purity(self.cov) else: self._purity = fock.purity(self._dm) return self._purity @@ -209,7 +209,10 @@ def fock(self) -> ComplexTensor: r"""Returns the Fock representation of the state.""" if self._dm is None and self._ket is None: _fock = fock.wigner_to_fock_state( - self.cov, self.means, shape=self.shape, return_dm=not self.is_hilbert_vector + self.cov, + self.means, + shape=self.shape, + return_dm=not self.is_hilbert_vector, ) if self.is_mixed: self._dm = _fock @@ -223,7 +226,7 @@ def fock(self) -> ComplexTensor: def number_means(self) -> RealVector: r"""Returns the mean photon number for each mode.""" if self.is_gaussian: - return gaussian.number_means(self.cov, self.means, settings.HBAR) + return gaussian.number_means(self.cov, self.means) return fock.number_means(tensor=self.fock, is_dm=self.is_mixed) @@ -233,7 +236,7 @@ def number_cov(self) -> RealMatrix: if not self.is_gaussian: raise NotImplementedError("number_cov not yet implemented for non-gaussian states") - return gaussian.number_cov(self.cov, self.means, settings.HBAR) + return gaussian.number_cov(self.cov, self.means) @property def norm(self) -> float: @@ -508,7 +511,9 @@ def __and__(self, other: State) -> State: cov = gaussian.join_covs([self.cov, other.cov]) means = gaussian.join_means([self.means, other.means]) return State( - cov=cov, means=means, modes=self.modes + [m + self.num_modes for m in other.modes] + cov=cov, + means=means, + modes=self.modes + [m + self.num_modes for m in other.modes], ) def __getitem__(self, item) -> State: @@ -581,11 +586,15 @@ def __eq__(self, other) -> bool: # pylint: disable=too-many-return-statements return True try: return np.allclose( - self.ket(cutoffs=other.cutoffs), other.ket(cutoffs=other.cutoffs), atol=1e-6 + self.ket(cutoffs=other.cutoffs), + other.ket(cutoffs=other.cutoffs), + atol=1e-6, ) except TypeError: return np.allclose( - self.dm(cutoffs=other.cutoffs), other.dm(cutoffs=other.cutoffs), atol=1e-6 + self.dm(cutoffs=other.cutoffs), + other.dm(cutoffs=other.cutoffs), + atol=1e-6, ) def __rshift__(self, other: Transformation) -> State: @@ -617,7 +626,8 @@ def __rmul__(self, other): """ if self.is_gaussian: warnings.warn( - "scalar multiplication forces conversion to fock representation", UserWarning + "scalar multiplication forces conversion to fock representation", + UserWarning, ) if self.is_pure: return State(ket=self.ket() * other) diff --git a/mrmustard/lab/abstract/transformation.py b/mrmustard/lab/abstract/transformation.py index 281791937..5a8323174 100644 --- a/mrmustard/lab/abstract/transformation.py +++ b/mrmustard/lab/abstract/transformation.py @@ -250,6 +250,7 @@ def choi(self, cutoffs: Sequence[int]): f"Invalid number of cutoffs: {len(cutoffs)} (expected {self.num_modes} or {4*self.num_modes})" ) if self.is_unitary: + shape = shape[: 2 * self.num_modes] U = self.U(shape[: self.num_modes]) Udual = self.U(shape[self.num_modes :]) return fock.U_to_choi(U, Udual) diff --git a/mrmustard/lab/gates.py b/mrmustard/lab/gates.py index f497e663c..2da05f71b 100644 --- a/mrmustard/lab/gates.py +++ b/mrmustard/lab/gates.py @@ -19,9 +19,9 @@ """ from typing import List, Optional, Sequence, Tuple, Union - +import numpy as np from mrmustard import settings -from mrmustard.lab.abstract import Transformation +from mrmustard.lab.abstract import State, Transformation from mrmustard.math import Math from mrmustard.physics import fock, gaussian from mrmustard.training import Parametrized @@ -45,6 +45,7 @@ "Attenuator", "Amplifier", "AdditiveNoise", + "PhaseNoise", ] @@ -92,7 +93,7 @@ def __init__( @property def d_vector(self): - return gaussian.displacement(self.x.value, self.y.value, settings.HBAR) + return gaussian.displacement(self.x.value, self.y.value) def U(self, cutoffs: Sequence[int]): r"""Returns the unitary representation of the Displacement gate using @@ -787,11 +788,11 @@ def __init__( @property def X_matrix(self): - return gaussian.loss_XYd(self.transmissivity.value, self.nbar.value, settings.HBAR)[0] + return gaussian.loss_XYd(self.transmissivity.value, self.nbar.value)[0] @property def Y_matrix(self): - return gaussian.loss_XYd(self.transmissivity.value, self.nbar.value, settings.HBAR)[1] + return gaussian.loss_XYd(self.transmissivity.value, self.nbar.value)[1] class Amplifier(Parametrized, Transformation): @@ -844,11 +845,11 @@ def __init__( @property def X_matrix(self): - return gaussian.amp_XYd(self.gain.value, self.nbar.value, settings.HBAR)[0] + return gaussian.amp_XYd(self.gain.value, self.nbar.value)[0] @property def Y_matrix(self): - return gaussian.amp_XYd(self.gain.value, self.nbar.value, settings.HBAR)[1] + return gaussian.amp_XYd(self.gain.value, self.nbar.value)[1] # pylint: disable=no-member @@ -897,4 +898,69 @@ def __init__( @property def Y_matrix(self): - return gaussian.noise_Y(self.noise.value, settings.HBAR) + return gaussian.noise_Y(self.noise.value) + + +class PhaseNoise(Parametrized, Transformation): + r"""The phase noise channel. + + The phase noise channel is a non-Gaussian transformation that is equivalent to + a random phase rotation. + + Args: + phase_stdev (float or List[float]): the standard deviation of the (wrapped) normal + distribution for the angle of the rotation + modes (optional, list(int)): the single mode this gate is applied to (default [0]) + """ + + def __init__( + self, + phase_stdev: Union[Optional[float], Optional[List[float]]] = 0.0, + phase_stdev_trainable: bool = False, + phase_stdev_bounds: Tuple[Optional[float], Optional[float]] = (0.0, None), + modes: Optional[List[int]] = None, + ): + super().__init__( + phase_stdev=phase_stdev, + phase_stdev_trainable=phase_stdev_trainable, + phase_stdev_bounds=phase_stdev_bounds, + ) + self._modes = modes or [0] + self.is_unitary = False + self.is_gaussian = False + self.short_name = "P~" + + # need to override primal because of the unconventional way + # the channel is defined in Fock representation + def primal(self, state): + idx = state.modes.index(self.modes[0]) + if state.is_pure: + ket = state.ket() + dm = fock.ket_to_dm(ket) + else: + dm = state.dm() + + # transpose dm so that the modes of interest are at the end + M = state.num_modes + indices = list(range(2 * M)) + indices.remove(idx) + indices.remove(idx + M) + indices += [idx, idx + M] + dm = math.transpose(dm, indices) + + coeff = math.cast( + math.exp( + -0.5 + * self.phase_stdev.value**2 + * math.arange(-dm.shape[-2] + 1, dm.shape[-1]) ** 2 + ), + dm.dtype, + ) + + for k in range(-dm.shape[-2] + 1, dm.shape[-1]): + diagonal = math.diag_part(dm, k=k) + diagonal *= coeff[k + dm.shape[-2] - 1] + dm = math.set_diag(dm, diagonal, k=k) + + # transpose dm back to the original order + return State(dm=math.transpose(dm, np.argsort(indices)), modes=state.modes) diff --git a/mrmustard/lab/states.py b/mrmustard/lab/states.py index ea10e1d73..e7545c2bd 100644 --- a/mrmustard/lab/states.py +++ b/mrmustard/lab/states.py @@ -45,8 +45,8 @@ class Vacuum(State): r"""The N-mode vacuum state.""" def __init__(self, num_modes: int): - cov = gaussian.vacuum_cov(num_modes, settings.HBAR) - means = gaussian.vacuum_means(num_modes, settings.HBAR) + cov = gaussian.vacuum_cov(num_modes) + means = gaussian.vacuum_means(num_modes) State.__init__(self, cov=cov, means=means) @@ -109,13 +109,13 @@ def __init__( self._modes = modes self._normalize = normalize - means = gaussian.displacement(self.x.value, self.y.value, settings.HBAR) - cov = gaussian.vacuum_cov(means.shape[-1] // 2, settings.HBAR) + means = gaussian.displacement(self.x.value, self.y.value) + cov = gaussian.vacuum_cov(means.shape[-1] // 2) State.__init__(self, cov=cov, means=means, cutoffs=cutoffs, modes=modes) @property def means(self): - return gaussian.displacement(self.x.value, self.y.value, settings.HBAR) + return gaussian.displacement(self.x.value, self.y.value) class SqueezedVacuum(Parametrized, State): @@ -176,13 +176,15 @@ def __init__( self._modes = modes self._normalize = normalize - cov = gaussian.squeezed_vacuum_cov(self.r.value, self.phi.value, settings.HBAR) - means = gaussian.vacuum_means(cov.shape[-1] // 2, settings.HBAR) + cov = gaussian.squeezed_vacuum_cov(self.r.value, self.phi.value) + means = gaussian.vacuum_means( + cov.shape[-1] // 2, + ) State.__init__(self, cov=cov, means=means, cutoffs=cutoffs) @property def cov(self): - return gaussian.squeezed_vacuum_cov(self.r.value, self.phi.value, settings.HBAR) + return gaussian.squeezed_vacuum_cov(self.r.value, self.phi.value) class TMSV(Parametrized, State): @@ -231,13 +233,16 @@ def __init__( self._modes = modes self._normalize = normalize - cov = gaussian.two_mode_squeezed_vacuum_cov(self.r.value, self.phi.value, settings.HBAR) - means = gaussian.vacuum_means(2, settings.HBAR) + cov = gaussian.two_mode_squeezed_vacuum_cov( + self.r.value, + self.phi.value, + ) + means = gaussian.vacuum_means(2) State.__init__(self, cov=cov, means=means, cutoffs=cutoffs) @property def cov(self): - return gaussian.two_mode_squeezed_vacuum_cov(self.r.value, self.phi.value, settings.HBAR) + return gaussian.two_mode_squeezed_vacuum_cov(self.r.value, self.phi.value) class Thermal(Parametrized, State): @@ -284,13 +289,13 @@ def __init__( self._modes = modes self._normalize = normalize - cov = gaussian.thermal_cov(self.nbar.value, settings.HBAR) - means = gaussian.vacuum_means(cov.shape[-1] // 2, settings.HBAR) + cov = gaussian.thermal_cov(self.nbar.value) + means = gaussian.vacuum_means(cov.shape[-1] // 2) State.__init__(self, cov=cov, means=means, cutoffs=cutoffs) @property def cov(self): - return gaussian.thermal_cov(self.nbar.value, settings.HBAR) + return gaussian.thermal_cov(self.nbar.value) class DisplacedSqueezed(Parametrized, State): @@ -371,17 +376,17 @@ def __init__( self._modes = modes self._normalize = normalize - cov = gaussian.squeezed_vacuum_cov(self.r.value, self.phi.value, settings.HBAR) - means = gaussian.displacement(self.x.value, self.y.value, settings.HBAR) + cov = gaussian.squeezed_vacuum_cov(self.r.value, self.phi.value) + means = gaussian.displacement(self.x.value, self.y.value) State.__init__(self, cov=cov, means=means, cutoffs=cutoffs, modes=modes) @property def cov(self): - return gaussian.squeezed_vacuum_cov(self.r.value, self.phi.value, settings.HBAR) + return gaussian.squeezed_vacuum_cov(self.r.value, self.phi.value) @property def means(self): - return gaussian.displacement(self.x.value, self.y.value, settings.HBAR) + return gaussian.displacement(self.x.value, self.y.value) class Gaussian(Parametrized, State): @@ -421,7 +426,7 @@ def __init__( eigenvalues: Vector = None, symplectic_trainable: bool = False, eigenvalues_trainable: bool = False, - eigenvalues_bounds: Tuple[Optional[float], Optional[float]] = (settings.HBAR / 2, None), + eigenvalues_bounds: Tuple[Optional[float], Optional[float]] = (None, None), modes: List[int] = None, cutoffs: Optional[Sequence[int]] = None, normalize: bool = False, @@ -440,14 +445,16 @@ def __init__( eigenvalues=eigenvalues, eigenvalues_trainable=eigenvalues_trainable, symplectic_trainable=symplectic_trainable, - eigenvalues_bounds=eigenvalues_bounds, + eigenvalues_bounds=(settings.HBAR / 2, None) + if eigenvalues_bounds == (None, None) + else eigenvalues_bounds, symplectic_bounds=(None, None), ) self._modes = modes self._normalize = normalize cov = gaussian.gaussian_cov(self.symplectic.value, self.eigenvalues.value) - means = gaussian.vacuum_means(cov.shape[-1] // 2, settings.HBAR) + means = gaussian.vacuum_means(cov.shape[-1] // 2) State.__init__(self, cov=cov, means=means, cutoffs=cutoffs) @property diff --git a/mrmustard/math/math_interface.py b/mrmustard/math/math_interface.py index 986c9171b..37255ad26 100644 --- a/mrmustard/math/math_interface.py +++ b/mrmustard/math/math_interface.py @@ -297,11 +297,12 @@ def diag(self, array: Tensor, k: int) -> Tensor: """ @abstractmethod - def diag_part(self, array: Tensor) -> Tensor: + def diag_part(self, array: Tensor, k: int) -> Tensor: r"""Returns the array of the main diagonal of array. Args: array (array): array to extract the main diagonal of + k (int): diagonal to extract Returns: array: array of the main diagonal of array @@ -668,6 +669,19 @@ def reshape(self, array: Tensor, shape: Sequence[int]) -> Tensor: array: reshaped array """ + @abstractmethod + def set_diag(self, array: Tensor, diag: Tensor, k: int) -> Tensor: + r"""Returns the array with the diagonal set to ``diag``. + + Args: + array (array): array to set the diagonal of + diag (array): diagonal to set + k (int): diagonal to set + + Returns: + array: array with the diagonal set to ``diag`` + """ + @abstractmethod def sin(self, array: Tensor) -> Tensor: r"""Returns the sine of ``array``. diff --git a/mrmustard/math/tensorflow.py b/mrmustard/math/tensorflow.py index 5be6eba03..8a0415892 100644 --- a/mrmustard/math/tensorflow.py +++ b/mrmustard/math/tensorflow.py @@ -81,7 +81,10 @@ def clip(self, array, a_min, a_max) -> tf.Tensor: return tf.clip_by_value(array, a_min, a_max) def concat(self, values: Sequence[tf.Tensor], axis: int) -> tf.Tensor: - return tf.concat(values, axis) + if any(tf.rank(v) == 0 for v in values): + return tf.stack(values, axis) + else: + return tf.concat(values, axis) def conj(self, array: tf.Tensor) -> tf.Tensor: return tf.math.conj(array) @@ -133,8 +136,8 @@ def det(self, matrix: tf.Tensor) -> tf.Tensor: def diag(self, array: tf.Tensor, k: int = 0) -> tf.Tensor: return tf.linalg.diag(array, k=k) - def diag_part(self, array: tf.Tensor) -> tf.Tensor: - return tf.linalg.diag_part(array) + def diag_part(self, array: tf.Tensor, k: int = 0) -> tf.Tensor: + return tf.linalg.diag_part(array, k=k) def einsum(self, string: str, *tensors) -> tf.Tensor: if type(string) is str: @@ -260,6 +263,9 @@ def real(self, array: tf.Tensor) -> tf.Tensor: def reshape(self, array: tf.Tensor, shape: Sequence[int]) -> tf.Tensor: return tf.reshape(array, shape) + def set_diag(self, array: tf.Tensor, diag: tf.Tensor, k: int) -> tf.Tensor: + return tf.linalg.set_diag(array, diag, k=k) + def sin(self, array: tf.Tensor) -> tf.Tensor: return tf.math.sin(array) @@ -338,9 +344,9 @@ def MultivariateNormalTriL(self, loc: Tensor, scale_tril: Tensor): # TODO: is a wrapper class better? @staticmethod - def DefaultEuclideanOptimizer() -> tf.keras.optimizers.Optimizer: + def DefaultEuclideanOptimizer() -> tf.keras.optimizers.legacy.Optimizer: r"""Default optimizer for the Euclidean parameters.""" - return tf.keras.optimizers.Adam(learning_rate=0.001) + return tf.keras.optimizers.legacy.Adam(learning_rate=0.001) def value_and_gradients( self, cost_fn: Callable, parameters: List[Trainable] diff --git a/mrmustard/physics/__init__.py b/mrmustard/physics/__init__.py index 37ce555f0..e8e91337c 100644 --- a/mrmustard/physics/__init__.py +++ b/mrmustard/physics/__init__.py @@ -21,7 +21,6 @@ """ from mrmustard.physics import fock, gaussian -from mrmustard import settings # pylint: disable=protected-access @@ -36,7 +35,7 @@ def fidelity(A, B) -> float: float: The fidelity between the two states. """ if A.is_gaussian and B.is_gaussian: - return gaussian.fidelity(A.means, A.cov, B.means, B.cov, settings.HBAR) + return gaussian.fidelity(A.means, A.cov, B.means, B.cov) return fock.fidelity(A.fock, B.fock, a_ket=A._ket is not None, b_ket=B._ket is not None) @@ -90,9 +89,6 @@ def overlap(A, B) -> float: float: the overlap between the two states """ raise NotImplementedError - if A.is_gaussian and B.is_gaussian: - return gaussian.overlap(A.means, A.cov, B.means, B.cov, settings.HBAR) - return fock.overlap(A.fock, B.fock, a_dm=A.is_mixed, b_dm=B.is_mixed) def von_neumann_entropy(A) -> float: @@ -105,7 +101,7 @@ def von_neumann_entropy(A) -> float: float: the Von Neumann entropy of the state """ if A.is_gaussian: - return gaussian.von_neumann_entropy(A.cov, settings.HBAR) + return gaussian.von_neumann_entropy(A.cov) return fock.von_neumann_entropy(A.fock, a_dm=A.is_mixed) @@ -120,9 +116,6 @@ def relative_entropy(A, B) -> float: float: the relative entropy between the two states """ raise NotImplementedError - if A.is_gaussian and B.is_gaussian: - return gaussian.relative_entropy(A.means, A.cov, B.means, B.cov, settings.HBAR) - return fock.relative_entropy(A.fock, B.fock, a_dm=A.is_mixed, b_dm=B.is_mixed) def trace_distance(A, B) -> float: @@ -136,5 +129,5 @@ def trace_distance(A, B) -> float: float: the trace distance between the two states """ if A.is_gaussian and B.is_gaussian: - return gaussian.trace_distance(A.means, A.cov, B.means, B.cov, settings.HBAR) + return gaussian.trace_distance(A.means, A.cov, B.means, B.cov) return fock.trace_distance(A.fock, B.fock, a_dm=A.is_mixed, b_dm=B.is_mixed) diff --git a/mrmustard/physics/bargmann.py b/mrmustard/physics/bargmann.py index f9513fe8f..92cc7e078 100644 --- a/mrmustard/physics/bargmann.py +++ b/mrmustard/physics/bargmann.py @@ -46,18 +46,14 @@ def wigner_to_bargmann_rho(cov, means): for a density matrix (i.e. for `M` modes, `A` has shape `2M x 2M` and `B` has shape `2M`). The order of the rows/columns of A and B corresponds to a density matrix with the usual ordering of the indices. - Note that here A and B are defined with inverted blocks with respect to the literature, - otherwise the density matrix would have the left and the right indices swapped once we convert to Fock. - By inverted blocks we mean that if A is normally defined as `A = [[A_00, A_01], [A_10, A_11]]`, - here we define it as `A = [[A_11, A_10], [A_01, A_00]]`. For `B` we have `B = [B_0, B_1] -> B = [B_1, B_0]`. + Note that here A and B are defined with respect to the literature. """ N = cov.shape[-1] // 2 - A = math.matmul( - cayley(pq_to_aadag(cov), c=0.5), math.Xmat(N) - ) # X on the right, so the index order will be rho_{left,right}: + A = math.matmul(math.Xmat(N), cayley(pq_to_aadag(cov), c=0.5)) Q, beta = wigner_to_husimi(cov, means) - B = math.solve(Q, beta) # no conjugate, so that the index order will be rho_{left,right} - C = math.exp(-0.5 * math.sum(math.conj(beta) * B)) / math.sqrt(math.det(Q)) + b = math.solve(Q, beta) + B = math.conj(b) + C = math.exp(-0.5 * math.sum(math.conj(beta) * b)) / math.sqrt(math.det(Q)) return A, B, C @@ -67,16 +63,16 @@ def wigner_to_bargmann_psi(cov, means): """ N = cov.shape[-1] // 2 A, B, C = wigner_to_bargmann_rho(cov, means) - # NOTE: with A_rho and B_rho defined with inverted blocks, we now keep the first half rather than the second - return A[:N, :N], B[:N], math.sqrt(C) + return ( + A[N:, N:], + B[N:], + math.sqrt(C), + ) # NOTE: c for th psi is to calculated from the global phase formula. def wigner_to_bargmann_Choi(X, Y, d): r"""Converts the wigner representation in terms of covariance matrix and mean vector into the Bargmann `A,B,C` triple - for a channel (i.e. for M modes, A has shape 4M x 4M and B has shape 4M). - We have freedom to choose the order of the indices of the Choi matrix by rearranging the `MxM` blocks of A and the M-subvectors of B. - Here we choose the order `[out_l, in_l out_r, in_r]` (`in_l` and `in_r` to be contracted with the left and right indices of the density matrix) - so that after the contraction the result has the right order `[out_l, out_r]`.""" + for a channel (i.e. for M modes, A has shape 4M x 4M and B has shape 4M).""" N = X.shape[-1] // 2 I2 = math.eye(2 * N, dtype=X.dtype) XT = math.transpose(X) @@ -94,14 +90,13 @@ def wigner_to_bargmann_Choi(X, Y, d): [[I, 1j * I, o, o], [o, o, I, -1j * I], [I, -1j * I, o, o], [o, o, I, 1j * I]] ) / np.sqrt(2) A = math.matmul(math.matmul(R, A), math.dagger(R)) - A = math.matmul(A, math.Xmat(2 * N)) # yes: X on the right + A = math.matmul(math.Xmat(2 * N), A) b = math.matvec(xi_inv, d) B = math.matvec(math.conj(R), math.concat([b, -math.matvec(XT, b)], axis=-1)) / math.sqrt( settings.HBAR, dtype=R.dtype ) - B = math.concat([B[2 * N :], B[: 2 * N]], axis=-1) # yes: opposite order C = math.exp(-0.5 * math.sum(d * b) / settings.HBAR) / math.sqrt(math.det(xi), dtype=b.dtype) - # now A and B have order [out_l, in_l out_r, in_r]. + # now A and B have order [out_r, in_r out_l, in_l]. return A, B, math.cast(C, "complex128") @@ -111,5 +106,4 @@ def wigner_to_bargmann_U(X, d): """ N = X.shape[-1] // 2 A, B, C = wigner_to_bargmann_Choi(X, math.zeros_like(X), d) - # NOTE: with A_Choi and B_Choi defined with inverted blocks, we now keep the first half rather than the second - return A[: 2 * N, : 2 * N], B[: 2 * N], math.sqrt(C) + return A[2 * N :, 2 * N :], B[2 * N :], math.sqrt(C) diff --git a/mrmustard/physics/fock.py b/mrmustard/physics/fock.py index 9fd1e049c..c4659d1bf 100644 --- a/mrmustard/physics/fock.py +++ b/mrmustard/physics/fock.py @@ -28,7 +28,9 @@ from mrmustard.math.caching import tensor_int_cache from mrmustard.math.lattice import strategies from mrmustard.math.mmtensor import MMTensor -from mrmustard.math.numba.compactFock_diagonal_amps import fock_representation_diagonal_amps +from mrmustard.math.numba.compactFock_diagonal_amps import ( + fock_representation_diagonal_amps, +) from mrmustard.physics.bargmann import ( wigner_to_bargmann_Choi, wigner_to_bargmann_psi, @@ -121,6 +123,10 @@ def wigner_to_fock_state( """ if return_dm: A, B, C = wigner_to_bargmann_rho(cov, means) + # NOTE: change the order of the index in AB + Xmat = math.Xmat(A.shape[-1] // 2) + A = math.matmul(math.matmul(Xmat, A), Xmat) + B = math.matvec(Xmat, B) return math.hermite_renormalized(A, B, C, shape=shape) else: # here we can apply max prob and max photons A, B, C = wigner_to_bargmann_psi(cov, means) @@ -165,6 +171,11 @@ def wigner_to_fock_Choi(X, Y, d, shape): Tensor: the fock representation of the Choi matrix """ A, B, C = wigner_to_bargmann_Choi(X, Y, d) + # NOTE: change the order of the index in AB + Xmat = math.Xmat(A.shape[-1] // 2) + A = math.matmul(math.matmul(Xmat, A), Xmat) + N = B.shape[-1] // 2 + B = math.concat([B[N:], B[:N]], axis=-1) return math.hermite_renormalized(A, B, C, shape=tuple(shape)) @@ -248,7 +259,7 @@ def U_to_choi(U: Tensor, Udual: Optional[Tensor] = None) -> Tensor: Tensor: the Choi tensor. The index order is going to be :math:`[\mathrm{out}_l, \mathrm{in}_l, \mathrm{out}_r, \mathrm{in}_r]` where :math:`\mathrm{in}_l` and :math:`\mathrm{in}_r` are to be contracted with the left and right indices of the density matrix. """ - return math.outer(U, Udual or math.conj(U)) + return math.outer(U, math.conj(U) if Udual is None else Udual) def fidelity(state_a, state_b, a_ket: bool, b_ket: bool) -> Scalar: @@ -290,7 +301,8 @@ def fidelity(state_a, state_b, a_ket: bool, b_ket: bool) -> Scalar: min_cutoffs = [ slice(min(a, b)) for a, b in zip( - state_a.shape[: len(state_a.shape) // 2], state_b.shape[: len(state_b.shape) // 2] + state_a.shape[: len(state_a.shape) // 2], + state_b.shape[: len(state_b.shape) // 2], ) ] state_a = state_a[tuple(min_cutoffs * 2)] @@ -358,40 +370,40 @@ def validate_contraction_indices(in_idx, out_idx, M, name): ) -def apply_kraus_to_ket(kraus, ket, kraus_in_idx, kraus_out_idx=None): +def apply_kraus_to_ket(kraus, ket, kraus_in_modes, kraus_out_modes=None): r"""Applies a kraus operator to a ket. It assumes that the ket is indexed as left_1, ..., left_n. - The kraus op has indices that contract with the ket (kraus_in_idx) and indices that are left over (kraus_out_idx). - The final index order will be sorted (note that an index appearing in both kraus_in_idx and kraus_out_idx will replace the original index). + The kraus op has indices that contract with the ket (kraus_in_modes) and indices that are left over (kraus_out_modes). + The final index order will be sorted (note that an index appearing in both kraus_in_modes and kraus_out_modes will replace the original index). Args: kraus (array): the kraus operator to be applied ket (array): the ket to which the operator is applied - kraus_in_idx (list of ints): the indices (counting from 0) of the kraus operator that contract with the ket - kraus_out_idx (list of ints): the indices (counting from 0) of the kraus operator that are leftover + kraus_in_modes (list of ints): the indices (counting from 0) of the kraus operator that contract with the ket + kraus_out_modes (list of ints): the indices (counting from 0) of the kraus operator that are leftover Returns: - array: the resulting ket with indices as kraus_out_idx + uncontracted ket indices + array: the resulting ket with indices as kraus_out_modes + uncontracted ket indices """ - if kraus_out_idx is None: - kraus_out_idx = kraus_in_idx + if kraus_out_modes is None: + kraus_out_modes = kraus_in_modes - if not set(kraus_in_idx).issubset(range(ket.ndim)): - raise ValueError("kraus_in_idx should be a subset of the ket indices.") + if not set(kraus_in_modes).issubset(range(ket.ndim)): + raise ValueError("kraus_in_modes should be a subset of the ket indices.") - # check that there are no repeated indices in kraus_in_idx and kraus_out_idx (separately) - validate_contraction_indices(kraus_in_idx, kraus_out_idx, ket.ndim, "kraus") + # check that there are no repeated indices in kraus_in_modes and kraus_out_modes (separately) + validate_contraction_indices(kraus_in_modes, kraus_out_modes, ket.ndim, "kraus") ket = MMTensor(ket, axis_labels=[f"in_left_{i}" for i in range(ket.ndim)]) kraus = MMTensor( kraus, - axis_labels=[f"out_left_{i}" for i in kraus_out_idx] - + [f"in_left_{i}" for i in kraus_in_idx], + axis_labels=[f"out_left_{i}" for i in kraus_out_modes] + + [f"in_left_{i}" for i in kraus_in_modes], ) # contract the operator with the ket. - # now the leftover indices are in the order kraus_out_idx + uncontracted ket indices + # now the leftover indices are in the order kraus_out_modes + uncontracted ket indices kraus_ket = kraus @ ket # sort kraus_ket.axis_labels by the int at the end of each label. @@ -401,30 +413,30 @@ def apply_kraus_to_ket(kraus, ket, kraus_in_idx, kraus_out_idx=None): return kraus_ket.transpose(new_axis_labels).tensor -def apply_kraus_to_dm(kraus, dm, kraus_in_idx, kraus_out_idx=None): +def apply_kraus_to_dm(kraus, dm, kraus_in_modes, kraus_out_modes=None): r"""Applies a kraus operator to a density matrix. It assumes that the density matrix is indexed as left_1, ..., left_n, right_1, ..., right_n. - The kraus operator has indices that contract with the density matrix (kraus_in_idx) and indices that are leftover (kraus_out_idx). + The kraus operator has indices that contract with the density matrix (kraus_in_modes) and indices that are leftover (kraus_out_modes). `kraus` will contract from the left and from the right with the density matrix. For right contraction the kraus op is conjugated. Args: kraus (array): the operator to be applied dm (array): the density matrix to which the operator is applied - kraus_in_idx (list of ints): the indices (counting from 0) of the kraus operator that contract with the density matrix - kraus_out_idx (list of ints): the indices (counting from 0) of the kraus operator that are leftover (default None, in which case kraus_out_idx = kraus_in_idx) + kraus_in_modes (list of ints): the indices (counting from 0) of the kraus operator that contract with the density matrix + kraus_out_modes (list of ints): the indices (counting from 0) of the kraus operator that are leftover (default None, in which case kraus_out_modes = kraus_in_modes) Returns: array: the resulting density matrix """ - if kraus_out_idx is None: - kraus_out_idx = kraus_in_idx + if kraus_out_modes is None: + kraus_out_modes = kraus_in_modes - if not set(kraus_in_idx).issubset(range(dm.ndim // 2)): - raise ValueError("kraus_in_idx should be a subset of the density matrix indices.") + if not set(kraus_in_modes).issubset(range(dm.ndim // 2)): + raise ValueError("kraus_in_modes should be a subset of the density matrix indices.") - # check that there are no repeated indices in kraus_in_idx and kraus_out_idx (separately) - validate_contraction_indices(kraus_in_idx, kraus_out_idx, dm.ndim // 2, "kraus") + # check that there are no repeated indices in kraus_in_modes and kraus_out_modes (separately) + validate_contraction_indices(kraus_in_modes, kraus_out_modes, dm.ndim // 2, "kraus") dm = MMTensor( dm, @@ -433,18 +445,19 @@ def apply_kraus_to_dm(kraus, dm, kraus_in_idx, kraus_out_idx=None): ) kraus = MMTensor( kraus, - axis_labels=[f"out_left_{i}" for i in kraus_out_idx] + [f"left_{i}" for i in kraus_in_idx], + axis_labels=[f"out_left_{i}" for i in kraus_out_modes] + + [f"left_{i}" for i in kraus_in_modes], ) kraus_conj = MMTensor( math.conj(kraus.tensor), - axis_labels=[f"out_right_{i}" for i in kraus_out_idx] - + [f"right_{i}" for i in kraus_in_idx], + axis_labels=[f"out_right_{i}" for i in kraus_out_modes] + + [f"right_{i}" for i in kraus_in_modes], ) # contract the kraus operator with the density matrix from the left and from the right. k_dm_k = kraus @ dm @ kraus_conj # now the leftover indices are in the order: - # out_left_idx + uncontracted left indices + uncontracted right indices + out_right_idx + # out_left_modes + uncontracted left indices + uncontracted right indices + out_right_modes # sort k_dm_k.axis_labels by the int at the end of each label, first left, then right N = k_dm_k.tensor.ndim // 2 @@ -478,9 +491,9 @@ def apply_choi_to_dm( if choi_out_modes is None: choi_out_modes = choi_in_modes if not set(choi_in_modes).issubset(range(dm.ndim // 2)): - raise ValueError("choi_in_idx should be a subset of the density matrix indices.") + raise ValueError("choi_in_modes should be a subset of the density matrix indices.") - # check that there are no repeated indices in kraus_in_idx and kraus_out_idx (separately) + # check that there are no repeated indices in kraus_in_modes and kraus_out_modes (separately) validate_contraction_indices(choi_in_modes, choi_out_modes, dm.ndim // 2, "choi") dm = MMTensor( @@ -497,7 +510,7 @@ def apply_choi_to_dm( ) # contract the choi matrix with the density matrix. - # now the leftover indices are in the order out_left_idx + out_right_idx + uncontracted left indices + uncontracted right indices + # now the leftover indices are in the order out_left_modes + out_right_modes + uncontracted left indices + uncontracted right indices choi_dm = choi @ dm # sort choi_dm.axis_labels by the int at the end of each label, first left, then right @@ -509,43 +522,43 @@ def apply_choi_to_dm( return choi_dm.transpose(left + right).tensor -def apply_choi_to_ket(choi, ket, choi_in_idx, choi_out_idx=None): +def apply_choi_to_ket(choi, ket, choi_in_modes, choi_out_modes=None): r"""Applies a choi operator to a ket. It assumes that the ket is indexed as left_1, ..., left_n. - The choi operator has indices that contract with the ket (choi_in_idx) and indices that are left over (choi_out_idx). - `choi` will contract choi_in_idx from the left and from the right with the ket. + The choi operator has indices that contract with the ket (choi_in_modes) and indices that are left over (choi_out_modes). + `choi` will contract choi_in_modes from the left and from the right with the ket. Args: choi (array): the choi operator to be applied ket (array): the ket to which the choi operator is applied - choi_in_idx (list of ints): the indices of the choi operator that contract with the ket - choi_out_idx (list of ints): the indices of the choi operator that re leftover + choi_in_modes (list of ints): the indices of the choi operator that contract with the ket + choi_out_modes (list of ints): the indices of the choi operator that re leftover Returns: array: the resulting ket """ - if choi_out_idx is None: - choi_out_idx = choi_in_idx + if choi_out_modes is None: + choi_out_modes = choi_in_modes - if not set(choi_in_idx).issubset(range(ket.ndim)): - raise ValueError("choi_in_idx should be a subset of the ket indices.") + if not set(choi_in_modes).issubset(range(ket.ndim)): + raise ValueError("choi_in_modes should be a subset of the ket indices.") - # check that there are no repeated indices in kraus_in_idx and kraus_out_idx (separately) - validate_contraction_indices(choi_in_idx, choi_out_idx, ket.ndim, "choi") + # check that there are no repeated indices in kraus_in_modes and kraus_out_modes (separately) + validate_contraction_indices(choi_in_modes, choi_out_modes, ket.ndim, "choi") ket = MMTensor(ket, axis_labels=[f"left_{i}" for i in range(ket.ndim)]) ket_dual = MMTensor(math.conj(ket.tensor), axis_labels=[f"right_{i}" for i in range(ket.ndim)]) choi = MMTensor( choi, - axis_labels=[f"out_left_{i}" for i in choi_out_idx] - + [f"left_{i}" for i in choi_in_idx] - + [f"out_right_{i}" for i in choi_out_idx] - + [f"right_{i}" for i in choi_in_idx], + axis_labels=[f"out_left_{i}" for i in choi_out_modes] + + [f"left_{i}" for i in choi_in_modes] + + [f"out_right_{i}" for i in choi_out_modes] + + [f"right_{i}" for i in choi_in_modes], ) # contract the choi matrix with the ket and its dual, like choi @ |ket> Tensor: Args: q (Vector): a vector containing the q points at which the function is evaluated (units of \sqrt{\hbar}) cutoff (int): maximum number of photons - hbar (optional): value of `\hbar`, defaults to Mr Mustard's internal value Returns: Tensor: a tensor of size ``len(q)*cutoff``. Each entry with index ``[i, j]`` represents the eigenstate evaluated @@ -779,7 +800,9 @@ def estimate_quadrature_axis(cutoff, minimum=5, period_resolution=20): def quadrature_distribution( - state: Tensor, quadrature_angle: float = 0.0, x: Vector = None, hbar: Optional[float] = None + state: Tensor, + quadrature_angle: float = 0.0, + x: Vector = None, ): r"""Given the ket or density matrix of a single-mode state, it generates the probability density distribution :math:`\tr [ \rho |x_\phi> Tuple[float, float]: +def sample_homodyne(state: Tensor, quadrature_angle: float = 0.0) -> Tuple[float, float]: r"""Given a single-mode state, it generates the pdf of :math:`\tr [ \rho |x_\phi> Matrix: +def vacuum_cov(num_modes: int) -> Matrix: r"""Returns the real covariance matrix of the vacuum state. Args: num_modes (int): number of modes - hbar (float): value of ``hbar`` Returns: Matrix: vacuum covariance matrix """ - return math.eye(num_modes * 2, dtype=math.float64) * hbar / 2 + return math.eye(num_modes * 2, dtype=math.float64) * settings.HBAR / 2 -def vacuum_means(num_modes: int, hbar: float) -> Tuple[Matrix, Vector]: +def vacuum_means(num_modes: int) -> Tuple[Matrix, Vector]: r"""Returns the real covariance matrix and real means vector of the vacuum state. Args: num_modes (int): number of modes - hbar (float): value of ``hbar`` Returns: Matrix, Vector: thermal state covariance matrix or means vector @@ -59,11 +57,10 @@ def vacuum_means(num_modes: int, hbar: float) -> Tuple[Matrix, Vector]: return displacement( math.zeros(num_modes, dtype="float64"), math.zeros(num_modes, dtype="float64"), - hbar, ) -def squeezed_vacuum_cov(r: Vector, phi: Vector, hbar: float) -> Matrix: +def squeezed_vacuum_cov(r: Vector, phi: Vector) -> Matrix: r"""Returns the real covariance matrix and real means vector of a squeezed vacuum state. The dimension depends on the dimensions of ``r`` and ``phi``. @@ -71,32 +68,30 @@ def squeezed_vacuum_cov(r: Vector, phi: Vector, hbar: float) -> Matrix: Args: r (vector): squeezing magnitude phi (vector): squeezing angle - hbar: value of ``hbar`` Returns: Matrix, Vector: thermal state covariance matrix or means vector """ S = squeezing_symplectic(r, phi) - return math.matmul(S, math.transpose(S)) * hbar / 2 + return math.matmul(S, math.transpose(S)) * settings.HBAR / 2 -def thermal_cov(nbar: Vector, hbar: float) -> Tuple[Matrix, Vector]: +def thermal_cov(nbar: Vector) -> Tuple[Matrix, Vector]: r"""Returns the real covariance matrix and real means vector of a thermal state. The dimension depends on the dimensions of ``nbar``. Args: nbar (vector): average number of photons per mode - hbar: value of ``hbar`` Returns: Matrix, Vector: thermal state covariance matrix or means vector """ - g = (2 * math.atleast_1d(nbar) + 1) * hbar / 2 + g = (2 * math.atleast_1d(nbar) + 1) * settings.HBAR / 2 return math.diag(math.concat([g, g], axis=-1)) -def two_mode_squeezed_vacuum_cov(r: Vector, phi: Vector, hbar: float) -> Matrix: +def two_mode_squeezed_vacuum_cov(r: Vector, phi: Vector) -> Matrix: r"""Returns the real covariance matrix and real means vector of a two-mode squeezed vacuum state. The dimension depends on the dimensions of ``r`` and ``phi``. @@ -104,14 +99,13 @@ def two_mode_squeezed_vacuum_cov(r: Vector, phi: Vector, hbar: float) -> Matrix: Args: r (vector): squeezing magnitude phi (vector): squeezing angle - hbar: value of hbar Returns: Matrix: two-mode squeezed state covariance matrix Vector: two-mode squeezed state means vector """ S = two_mode_squeezing_symplectic(r, phi) - return math.matmul(S, math.transpose(S)) * hbar / 2 + return math.matmul(S, math.transpose(S)) * settings.HBAR / 2 def gaussian_cov(symplectic: Matrix, eigenvalues: Vector = None) -> Matrix: @@ -192,14 +186,13 @@ def squeezing_symplectic(r: Union[Scalar, Vector], phi: Union[Scalar, Vector]) - ) -def displacement(x: Union[Scalar, Vector], y: Union[Scalar, Vector], hbar: float) -> Vector: +def displacement(x: Union[Scalar, Vector], y: Union[Scalar, Vector]) -> Vector: r"""Returns the displacement vector for a displacement by :math:`alpha = x + iy`. The dimension depends on the dimensions of ``x`` and ``y``. Args: x (scalar or vector): real part of displacement y (scalar or vector): imaginary part of displacement - hbar: value of hbar Returns: Vector: displacement vector of a displacement gate @@ -210,7 +203,7 @@ def displacement(x: Union[Scalar, Vector], y: Union[Scalar, Vector], hbar: float x = math.tile(x, y.shape) if y.shape[-1] == 1: y = math.tile(y, x.shape) - return math.sqrt(2 * hbar, dtype=x.dtype) * math.concat([x, y], axis=0) + return math.sqrt(2 * settings.HBAR, dtype=x.dtype) * math.concat([x, y], axis=0) def beam_splitter_symplectic(theta: Scalar, phi: Scalar) -> Matrix: @@ -419,7 +412,6 @@ def CPTP( d (Vector): displacement vector of the CPTP channel state_modes (Sequence[int]): modes the state is defined on transf_modes (Sequence[int]): modes on which the channel acts - hbar (float): value of hbar Returns: Tuple[Matrix, Vector]: the covariance matrix and the means vector of the state after the CPTP channel @@ -447,7 +439,7 @@ def CPTP( def loss_XYd( - transmissivity: Union[Scalar, Vector], nbar: Union[Scalar, Vector], hbar: float + transmissivity: Union[Scalar, Vector], nbar: Union[Scalar, Vector] ) -> Tuple[Matrix, Matrix, None]: r"""Returns the ``X``, ``Y`` matrices and the ``d`` vector for the noisy loss (attenuator) channel. @@ -470,12 +462,12 @@ def loss_XYd( raise ValueError("transmissivity must be between 0 and 1") x = math.sqrt(transmissivity) X = math.diag(math.concat([x, x], axis=0)) - y = (1 - transmissivity) * (2 * nbar + 1) * hbar / 2 + y = (1 - transmissivity) * (2 * nbar + 1) * settings.HBAR / 2 Y = math.diag(math.concat([y, y], axis=0)) return X, Y, None -def amp_XYd(gain: Union[Scalar, Vector], nbar: Union[Scalar, Vector], hbar: float) -> Matrix: +def amp_XYd(gain: Union[Scalar, Vector], nbar: Union[Scalar, Vector]) -> Matrix: r"""Returns the ``X``, ``Y`` matrices and the d vector for the noisy amplifier channel. The quantum limited amplifier channel is recovered for ``nbar = 0.0``. @@ -493,12 +485,12 @@ def amp_XYd(gain: Union[Scalar, Vector], nbar: Union[Scalar, Vector], hbar: floa raise ValueError("Gain must be larger than 1") x = math.sqrt(gain) X = math.diag(math.concat([x, x], axis=0)) - y = (gain - 1) * (2 * nbar + 1) * hbar / 2 + y = (gain - 1) * (2 * nbar + 1) * settings.HBAR / 2 Y = math.diag(math.concat([y, y], axis=0)) return X, Y, None -def noise_Y(noise: Union[Scalar, Vector], hbar: float) -> Matrix: +def noise_Y(noise: Union[Scalar, Vector]) -> Matrix: r"""Returns the ``X``, ``Y`` matrices and the d vector for the additive noise channel ``(Y = noise * (\hbar / 2) * I)`` Args: @@ -507,7 +499,7 @@ def noise_Y(noise: Union[Scalar, Vector], hbar: float) -> Matrix: Returns: Tuple[None, Matrix, None]: the ``X``, ``Y`` matrices and the ``d`` vector of the noise channel. """ - return math.diag(math.concat([noise, noise], axis=0)) * hbar / 2 + return math.diag(math.concat([noise, noise], axis=0)) * settings.HBAR / 2 def compose_channels_XYd( @@ -611,13 +603,12 @@ def general_dyne( # ~~~~~~~~~ # utilities # ~~~~~~~~~ -def number_means(cov: Matrix, means: Vector, hbar: float) -> Vector: +def number_means(cov: Matrix, means: Vector) -> Vector: r"""Returns the photon number means vector given a Wigner covariance matrix and a means vector. Args: cov: the Wigner covariance matrix means: the Wigner means vector - hbar: the value of the Planck constant Returns: Vector: the photon number means vector @@ -628,17 +619,16 @@ def number_means(cov: Matrix, means: Vector, hbar: float) -> Vector: + means[N:] ** 2 + math.diag_part(cov[:N, :N]) + math.diag_part(cov[N:, N:]) - - hbar - ) / (2 * hbar) + - settings.HBAR + ) / (2 * settings.HBAR) -def number_cov(cov: Matrix, means: Vector, hbar: float) -> Matrix: +def number_cov(cov: Matrix, means: Vector) -> Matrix: r"""Returns the photon number covariance matrix given a Wigner covariance matrix and a means vector. Args: cov: the Wigner covariance matrix means: the Wigner means vector - hbar: the value of the Planck constant Returns: Matrix: the photon number covariance matrix @@ -646,9 +636,9 @@ def number_cov(cov: Matrix, means: Vector, hbar: float) -> Matrix: N = means.shape[-1] // 2 mCm = cov * means[:, None] * means[None, :] dd = math.diag(math.diag_part(mCm[:N, :N] + mCm[N:, N:] + mCm[:N, N:] + mCm[N:, :N])) / ( - 2 * hbar**2 # TODO: sum(diag_part) is better than diag_part(sum) + 2 * settings.HBAR**2 # TODO: sum(diag_part) is better than diag_part(sum) ) - CC = (cov**2 + mCm) / (2 * hbar**2) + CC = (cov**2 + mCm) / (2 * settings.HBAR**2) return ( CC[:N, :N] + CC[N:, N:] + CC[:N, N:] + CC[N:, :N] + dd - 0.25 * math.eye(N, dtype=CC.dtype) ) @@ -656,7 +646,7 @@ def number_cov(cov: Matrix, means: Vector, hbar: float) -> Matrix: def is_mixed_cov(cov: Matrix) -> bool: # TODO: deprecate r"""Returns ``True`` if the covariance matrix is mixed, ``False`` otherwise.""" - return not is_pure_cov(math.asnumpy(cov), hbar=settings.HBAR) + return not is_pure_cov(math.asnumpy(cov)) def trace(cov: Matrix, means: Vector, Bmodes: Sequence[int]) -> Tuple[Matrix, Vector]: @@ -720,7 +710,7 @@ def partition_means(means: Vector, Amodes: Sequence[int]) -> Tuple[Vector, Vecto return math.gather(means, Aindices), math.gather(means, Bindices) -def purity(cov: Matrix, hbar: float) -> Scalar: +def purity(cov: Matrix) -> Scalar: r"""Returns the purity of the state with the given covariance matrix. Args: @@ -729,28 +719,27 @@ def purity(cov: Matrix, hbar: float) -> Scalar: Returns: float: the purity """ - return 1 / math.sqrt(math.det((2 / hbar) * cov)) + return 1 / math.sqrt(math.det((2 / settings.HBAR) * cov)) -def symplectic_eigenvals(cov: Matrix, hbar: float) -> Any: +def symplectic_eigenvals(cov: Matrix) -> Any: r"""Returns the sympletic eigenspectrum of a covariance matrix. For a pure state, we expect the sympletic eigenvalues to be 1. Args: cov (Matrix): the covariance matrix - hbar (float): the value of the Planck constant Returns: List[float]: the sympletic eigenvalues """ J = math.J(cov.shape[-1] // 2) # create a sympletic form - M = math.matmul(1j * J, cov * (2 / hbar)) + M = math.matmul(1j * J, cov * (2 / settings.HBAR)) vals = math.eigvals(M) # compute the eigenspectrum return math.abs(vals[::2]) # return the even eigenvalues # TODO: sort? -def von_neumann_entropy(cov: Matrix, hbar: float) -> float: +def von_neumann_entropy(cov: Matrix) -> float: r"""Returns the Von Neumann entropy. For a pure state, we expect the Von Neumann entropy to be 0. @@ -767,12 +756,12 @@ def von_neumann_entropy(cov: Matrix, hbar: float) -> float: def g(x): return math.xlogy((x + 1) / 2, (x + 1) / 2) - math.xlogy((x - 1) / 2, (x - 1) / 2 + 1e-9) - symp_vals = symplectic_eigenvals(cov, hbar) + symp_vals = symplectic_eigenvals(cov) entropy = math.sum(g(symp_vals)) return entropy -def fidelity(mu1: Vector, cov1: Matrix, mu2: Vector, cov2: Matrix, hbar=2.0) -> float: +def fidelity(mu1: Vector, cov1: Matrix, mu2: Vector, cov2: Matrix) -> float: r"""Returns the fidelity of two gaussian states. Reference: `arXiv:2102.05748 `_, equations 95-99. @@ -788,12 +777,14 @@ def fidelity(mu1: Vector, cov1: Matrix, mu2: Vector, cov2: Matrix, hbar=2.0) -> float: the fidelity """ - cov1 = math.cast(cov1 / hbar, "complex128") # convert to units where hbar = 1 - cov2 = math.cast(cov2 / hbar, "complex128") # convert to units where hbar = 1 + cov1 = math.cast(cov1 / settings.HBAR, "complex128") # convert to units where hbar = 1 + cov2 = math.cast(cov2 / settings.HBAR, "complex128") # convert to units where hbar = 1 mu1 = math.cast(mu1, "complex128") mu2 = math.cast(mu2, "complex128") - deltar = (mu2 - mu1) / math.sqrt(hbar, dtype=mu1.dtype) # convert to units where hbar = 1 + deltar = (mu2 - mu1) / math.sqrt( + settings.HBAR, dtype=mu1.dtype + ) # convert to units where hbar = 1 J = math.J(cov1.shape[0] // 2) I = math.eye(cov1.shape[0]) J = math.cast(J, "complex128") @@ -844,7 +835,7 @@ def physical_partial_transpose(cov: Matrix, modes: Sequence[int]) -> Matrix: return cov * mat[:, None] * mat[None, :] -def log_negativity(cov: Matrix, hbar: float) -> float: +def log_negativity(cov: Matrix) -> float: r"""Returns the log_negativity of a Gaussian state. Reference: `https://arxiv.org/pdf/quant-ph/0102117.pdf `_ , Equation 57, 61. @@ -855,7 +846,7 @@ def log_negativity(cov: Matrix, hbar: float) -> float: Returns: float: the log-negativity """ - vals = symplectic_eigenvals(cov, hbar) / (hbar / 2) + vals = symplectic_eigenvals(cov) / (settings.HBAR / 2) vals_filtered = math.boolean_mask( vals, vals < 1.0 ) # Get rid of terms that would lead to zero contribution. diff --git a/mrmustard/settings.py b/mrmustard/settings.py new file mode 100644 index 000000000..8036c4763 --- /dev/null +++ b/mrmustard/settings.py @@ -0,0 +1,295 @@ +# 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. + +"""A module containing the settings. +""" + +from rich import print +import rich.table +import numpy as np + +__all__ = ["Settings", "settings"] + + +class ImmutableSetting: + r"""A setting that becomes immutable after the first time its value is queried. + + Args: + value (any): the default value of this setting + name (str): the name of this setting + """ + + def __init__(self, value: any, name: str) -> None: + self._value = value + self._name = name + self._is_immutable = False + + @property + def name(self): + r"""The name of this setting.""" + return self._name + + @property + def value(self): + r"""The value of this setting.""" + self._is_immutable = True + return self._value + + @value.setter + def value(self, value): + if self._is_immutable: + raise ValueError(f"Cannot change the value of `settings.{self.name}`.") + self._value = value + + +# pylint: disable=too-many-instance-attributes +class Settings: + r"""A class containing various settings that are used by Mr Mustard throughout a session. + + Some of these settings (such as those representing cutoff values) can be changed at any time, + while others (such as the value of the Planck constant) can only be changed before being + queried for the first time. + + .. code-block:: + + from mrmustard import settings + + >>> settings.AUTOCUTOFF_MAX_CUTOFF # check the default values + 100 + + >>> settings.AUTOCUTOFF_MAX_CUTOFF = 150 # update to new values + >>> settings.AUTOCUTOFF_MAX_CUTOFF + 150 + """ + + def __new__(cls): # singleton + if not hasattr(cls, "instance"): + cls.instance = super(Settings, cls).__new__(cls) + return cls.instance + + def __init__(self): + self._backend = "tensorflow" + self._hbar = ImmutableSetting(2.0, "HBAR") + self._debug = False + self._autocutoff_probability = 0.999 # capture at least 99.9% of the probability + self._autocutoff_max_cutoff = 100 + self._autocutoff_min_cutoff = 1 + self._circuit_decimals = 3 + self._discretization_method = "iterative" + # use cutoff=5 for each mode when determining if two transformations in fock repr are equal + # 3 is enough to include a full step of the rec relations + self._eq_transformation_cutoff = 3 + self._eq_transformation_rtol_fock = 1e-3 + self._eq_transformation_rtol_gauss = 1e-6 + # for the detectors + self._pnr_internal_cutoff = 50 + self._homodyne_squeezing = 10.0 + # misc + self._progressbar = True + self._seed = np.random.randint(0, 2**31 - 1) + self.rng = np.random.default_rng(self._seed) + self._default_bs_method = "vanilla" # can be 'vanilla' or 'schwinger' + + @property + def AUTOCUTOFF_MAX_CUTOFF(self): + r"""The maximum value for autocutoff. Default is ``100``.""" + return self._autocutoff_max_cutoff + + @AUTOCUTOFF_MAX_CUTOFF.setter + def AUTOCUTOFF_MAX_CUTOFF(self, value: str): + self._autocutoff_max_cutoff = value + + @property + def AUTOCUTOFF_MIN_CUTOFF(self): + r"""The minimum value for autocutoff. Default is ``1``.""" + return self._autocutoff_min_cutoff + + @AUTOCUTOFF_MIN_CUTOFF.setter + def AUTOCUTOFF_MIN_CUTOFF(self, value: str): + self._autocutoff_min_cutoff = value + + @property + def AUTOCUTOFF_PROBABILITY(self): + r"""The autocutoff probability. Default is ``0.999``.""" + return self._autocutoff_probability + + @AUTOCUTOFF_PROBABILITY.setter + def AUTOCUTOFF_PROBABILITY(self, value: str): + self._autocutoff_probability = value + + @property + def BACKEND(self): + r"""The backend which is used. Default is ``tensorflow``. + + Can be either ``'tensorflow'`` or ``'torch'``. + """ + return self._backend + + @BACKEND.setter + def BACKEND(self, value: str): + if value not in ["tensorflow", "torch"]: # pragma: no cover + raise ValueError("Backend must be either 'tensorflow' or 'torch'") + self._backend = value + + @property + def CIRCUIT_DECIMALS(self): + r"""The number of decimals displayed when drawing a circuit with parameters. Default is ``3``.""" + return self._circuit_decimals + + @CIRCUIT_DECIMALS.setter + def CIRCUIT_DECIMALS(self, value: str): + self._circuit_decimals = value + + @property + def DEBUG(self): + r"""Whether or not to print the vector of means and the covariance matrix alongside the + html representation of a state. Default is ``False``. + """ + return self._debug + + @DEBUG.setter + def DEBUG(self, value: str): + self._debug = value + + @property + def DISCRETIZATION_METHOD(self): + r"""The method used to discretize the Wigner function. Default is ``iterative``. + + Can be either ``'iterative'`` or ``'clenshaw'``. + """ + return self._discretization_method + + @DISCRETIZATION_METHOD.setter + def DISCRETIZATION_METHOD(self, value: str): + self._discretization_method = value + + @property + def DEFAULT_BS_METHOD(self): + r"""The default method for computing the transformation operated by a beam splitter in + the Fock basis . Default is ``vanilla``. + + Can be either ``'vanilla'`` or ``'schwinger'``. + """ + return self._default_bs_method + + @DEFAULT_BS_METHOD.setter + def DEFAULT_BS_METHOD(self, value: str): + self._default_bs_method = value + + @property + def EQ_TRANSFORMATION_CUTOFF(self): + r"""The cutoff used when comparing two transformations via the Choi–Jamiolkowski + isomorphism. Default is ``3``.""" + return self._eq_transformation_cutoff + + @EQ_TRANSFORMATION_CUTOFF.setter + def EQ_TRANSFORMATION_CUTOFF(self, value: str): + self._eq_transformation_cutoff = value + + @property + def EQ_TRANSFORMATION_RTOL_FOCK(self): + r"""The relative tolerance used when comparing two transformations via the Choi–Jamiolkowski + isomorphism. Default is ``1e-3``.""" + return self._eq_transformation_rtol_fock + + @EQ_TRANSFORMATION_RTOL_FOCK.setter + def EQ_TRANSFORMATION_RTOL_FOCK(self, value: str): + self._eq_transformation_rtol_fock = value + + @property + def EQ_TRANSFORMATION_RTOL_GAUSS(self): + r"""The relative tolerance used when comparing two transformations on Gaussian states. + Default is ``1e-6``.""" + return self._eq_transformation_rtol_gauss + + @EQ_TRANSFORMATION_RTOL_GAUSS.setter + def EQ_TRANSFORMATION_RTOL_GAUSS(self, value: str): + self._eq_transformation_rtol_gauss = value + + @property + def HBAR(self): + r"""The value of the Planck constant. Default is ``2``. + + Cannot be changed after its value is queried for the first time. + """ + return self._hbar.value + + @HBAR.setter + def HBAR(self, value: str): + self._hbar.value = value + + @property + def HOMODYNE_SQUEEZING(self): + r"""The value of squeezing for homodyne measurements. Default is ``10``.""" + return self._homodyne_squeezing + + @HOMODYNE_SQUEEZING.setter + def HOMODYNE_SQUEEZING(self, value: str): + self._homodyne_squeezing = value + + @property + def PNR_INTERNAL_CUTOFF(self): + r"""The cutoff used when computing the output of a PNR detection. Default is ``50``.""" + return self._pnr_internal_cutoff + + @PNR_INTERNAL_CUTOFF.setter + def PNR_INTERNAL_CUTOFF(self, value: str): + self._pnr_internal_cutoff = value + + @property + def PROGRESSBAR(self): + r"""Whether or not to display the progress bar when performing training. Default is ``True``.""" + return self._progressbar + + @PROGRESSBAR.setter + def PROGRESSBAR(self, value: str): + self._progressbar = value + + @property + def SEED(self): + r"""Returns the seed value if set, otherwise returns a random seed.""" + if self._seed is None: + self._seed = np.random.randint(0, 2**31 - 1) + self.rng = np.random.default_rng(self._seed) + return self._seed + + @SEED.setter + def SEED(self, value): + self._seed = value + self.rng = np.random.default_rng(self._seed) + + # use rich.table to print the settings + def __repr__(self) -> str: + r"""Returns a string representation of the settings.""" + + # attributes that should not be displayed in the table + not_displayed = ["rng"] + + table = rich.table.Table(title="MrMustard Settings") + table.add_column("Setting") + table.add_column("Value") + + for key, val in self.__dict__.items(): + if key in not_displayed: + continue + key = key.upper()[1:] + value = str(val._value) if isinstance(val, ImmutableSetting) else str(val) + table.add_row(key, value) + + print(table) + return "" + + +settings = Settings() +"""Settings object.""" diff --git a/mrmustard/training/trainer.py b/mrmustard/training/trainer.py index 738b6b8a2..79b3b1e12 100644 --- a/mrmustard/training/trainer.py +++ b/mrmustard/training/trainer.py @@ -71,8 +71,8 @@ def cost_fn(circ=make_circ(0.1), y_targ=0.): cost_fn=cost_fn, device_factory=make_circ, tasks=[ - {'x': 0.1, 'euclidean_lr': 0.005, 'max_steps': 50, 'HBAR': 1.}, - {'x': -0.7, 'euclidean_lr': 0.1, 'max_steps': 2, 'HBAR': 2.}, + {'x': 0.1, 'euclidean_lr': 0.005, 'max_steps': 50}, + {'x': -0.7, 'euclidean_lr': 0.1, 'max_steps': 2}, ], y_targ=0.35, symplectic_lr=0.05, @@ -313,8 +313,8 @@ def cost_fn(circ=make_circ(0.1), y_targ=0.): cost_fn=cost_fn, device_factory=make_circ, tasks=[ - {'x': 0.1, 'euclidean_lr': 0.005, 'max_steps': 50, 'HBAR': 1.}, - {'x': -0.7, 'euclidean_lr': 0.1, 'max_steps': 2, 'HBAR': 2.}, + {'x': 0.1, 'euclidean_lr': 0.005, 'max_steps': 50}, + {'x': -0.7, 'euclidean_lr': 0.1, 'max_steps': 2}, ], y_targ=0.35, symplectic_lr=0.05, diff --git a/mrmustard/utils/graphics.py b/mrmustard/utils/graphics.py index e3b98634d..f0eb4117f 100644 --- a/mrmustard/utils/graphics.py +++ b/mrmustard/utils/graphics.py @@ -113,12 +113,15 @@ def mikkel_plot( xvec = np.linspace(*xbounds, plot_args["resolution"]) pvec = np.linspace(*ybounds, plot_args["resolution"]) - W, X, P = wigner_discretized(rho, xvec, pvec, settings.HBAR) + W, X, P = wigner_discretized(rho, xvec, pvec) ### PLOTTING ### fig, ax = plt.subplots( - 2, 2, figsize=(6, 6), gridspec_kw={"width_ratios": [2, 1], "height_ratios": [1, 2]} + 2, + 2, + figsize=(6, 6), + gridspec_kw={"width_ratios": [2, 1], "height_ratios": [1, 2]}, ) plt.subplots_adjust(wspace=0.05, hspace=0.05) diff --git a/mrmustard/utils/wigner.py b/mrmustard/utils/wigner.py index 4941b6d0b..bfe4d10ad 100644 --- a/mrmustard/utils/wigner.py +++ b/mrmustard/utils/wigner.py @@ -18,49 +18,158 @@ from numba import njit from mrmustard import settings +from mrmustard.math import Math + + +__all__ = ["wigner_discretized"] + +math = Math() + +# ~~~~~~~ +# Helpers +# ~~~~~~~ @njit -def wigner_discretized(rho, qvec, pvec, hbar=settings.HBAR): +def make_grid(q_vec, p_vec, hbar): # pragma: no cover + r"""Returns two coordinate matrices `Q` and `P` from coordinate vectors + `q_vec` and `p_vec`, along with the grid over which Wigner functions can be + discretized. + """ + Q = np.outer(q_vec, np.ones_like(p_vec)) + P = np.outer(np.ones_like(q_vec), p_vec) + return Q, P, (Q + P * 1.0j) / np.sqrt(2 * hbar) + + +@njit +def _wig_laguerre_val(L, x, diag): # pragma: no cover + """Returns the coefficient `c_L = sum_n rho_{n,L+n} Z_n^L` used + by `_wigner_discretized_clenshaw`. The evaluation uses the Clenshaw recursion. + """ + if len(diag) == 2: + y0 = np.array([[diag[0]]], dtype=np.complex128) + y1 = np.array([[diag[1]]], dtype=np.complex128) + else: + k = len(diag) + y0 = np.array([[diag[-2]]], dtype=np.complex128) + y1 = np.array([[diag[-1]]], dtype=np.complex128) + for i in range(3, len(diag) + 1): + k -= 1 + temp_y0 = y0 + y0 = diag[-i] - y1 * (float((k - 1) * (L + k - 1)) / ((L + k) * k)) ** 0.5 + y1 = temp_y0 - y1 * ((L + 2 * k - 1) - x) * ((L + k) * k) ** -0.5 + + return y0 - y1 * ((L + 1) - x) * (L + 1) ** -0.5 + + +# ~~~~~~~ +# Methods +# ~~~~~~~ + + +def wigner_discretized(rho, q_vec, p_vec): r"""Calculates the discretized Wigner function for a single mode. - Adapted from `strawberryfields ` + The supported discretization methods are: + + * ``iterative`` (default): Uses an iterative method to calculate the Wigner + coefficients :math:`W_{mn}` in :math:`W = \sum_{mn} W_{mn} |m\rangle\langle n|`. + This method is recommended for systems with low numbers of excitations (``n\leq50``). + * ``clenshaw``: Uses Clenshaw summations to improve the performance for systems + with large numbers of excitations (``n\leq50``). + + The discretization method can be changed by moodifying the `Settings` object. + + .. code:: + + >>> settings.DISCRETIZATION_METHOD # default method + "iterative" + + >>> settings.DISCRETIZATION_METHOD = "clenshaw" # change method + + These methods are adapted versions of the 'iterative' and 'clenshaw' methods of the + Wigner function discretization routine provided in + QuTiP `_, + which is released under the BSD license, with the following copyright notice: + + Copyright (C) 2011 and later, P.D. Nation, J.R. Johansson, + A.J.G. Pitchford, C. Granade, and A.L. Grimsmo. All rights reserved. Args: rho (complex array): the density matrix of the state in Fock representation - qvec (array): array of discretized :math:`q` quadrature values - pvec (array): array of discretized :math:`p` quadrature values - hbar (optional float): the value of `\hbar`, defaults to ``settings.HBAR``. + q_vec (array): array of discretized :math:`q` quadrature values + p_vec (array): array of discretized :math:`p` quadrature values - Retunrs: + Returns: tuple(array, array, array): array containing the discretized Wigner function, and the Q and P coordinates (in meshgrid form) in which the function is calculated """ + hbar = settings.HBAR + method = settings.DISCRETIZATION_METHOD + + rho = math.asnumpy(rho) + if method == "iterative": + return _wigner_discretized_iterative(rho, q_vec, p_vec, hbar) + return _wigner_discretized_clenshaw(rho, q_vec, p_vec, hbar) + + +@njit +def _wigner_discretized_clenshaw(rho, q_vec, p_vec, hbar): # pragma: no cover + r"""Calculates the Wigner function as + :math:`W = C(x) \sum_L c_L (2x)^L / sqrt(L!)`, where: + + * :math:`x = (q + ip)`, for ``q`` and ``p`` in ``q_vec`` and ``p_vec`` + respectively + * :math:`C(x) = e^{-x**2/(2\pi)}` + * :math:`L` is the dimension of ``rho`` + * :math:`c_L = \sum_n \rho_{n,L+n} Z_n^L` + * :math:`Z_n^L = (-1)^n sqrt(L!n!/(L+n)!) Lag(n,L,x)` + * :math:`LaguerreL(n,L,x)` + """ + cutoff = len(rho) + Q, P, grid = make_grid(q_vec, p_vec, hbar) + + A = 2 * grid + B = np.abs(A) + B *= B + + w0 = (2 * rho[0, -1]) * np.ones_like(A) + rho2 = rho * (2 * np.ones((cutoff, cutoff)) - np.diag(np.ones(cutoff))) + + L = cutoff - 1 + for j in range(1, cutoff): + c_L = _wig_laguerre_val(L - j, B, np.diag(rho2, L - j)) + w0 = c_L + w0 * A * (L - j + 1) ** -0.5 - Q = np.outer(qvec, np.ones_like(pvec)) - P = np.outer(np.ones_like(qvec), pvec) + return w0.real * np.exp(-B * 0.5) / np.pi / hbar, Q, P - cutoff = rho.shape[-1] - A = (Q + P * 1.0j) / np.sqrt(2 * hbar) - Wmat = np.zeros((2, cutoff) + A.shape, dtype=np.complex128) +@njit +def _wigner_discretized_iterative(rho, q_vec, p_vec, hbar): # pragma: no cover + cutoff = len(rho) + Q, P, grid = make_grid(q_vec, p_vec, hbar) + Wmat = np.zeros((2, cutoff) + grid.shape, dtype=np.complex128) - # Wigner function for |0><0| - Wmat[0, 0] = np.exp(-2.0 * np.abs(A) ** 2) / np.pi + # W = 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-version = "3.16.0" +version = "3.17.0" description = "Backport of pathlib-compatible object wrapper for zip files" -category = "main" optional = false python-versions = ">=3.8" files = [ - {file = "zipp-3.16.0-py3-none-any.whl", hash = "sha256:5dadc3ad0a1f825fe42ce1bce0f2fc5a13af2e6b2d386af5b0ff295bc0a287d3"}, - {file = "zipp-3.16.0.tar.gz", hash = "sha256:1876cb065531855bbe83b6c489dcf69ecc28f1068d8e95959fe8bbc77774c941"}, + {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"}, + {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"}, ] [package.extras] -docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] -testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-ruff"] +docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"] +testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] [extras] ray = ["ray", "scikit-optimize"] @@ -3442,4 +3505,4 @@ ray = ["ray", "scikit-optimize"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.11" -content-hash = "4bc682389a785eeb023c5eae11a3800b7e79cc3405421797fcf1bc5d6b19ef89" +content-hash = "56fb878d0d037e87a530b1fc5b5579445e1f29ad525f80decf6a596d738a942e" diff --git a/pyproject.toml b/pyproject.toml index 1262f969a..a52c94fb2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "mrmustard" -version = "0.5.0" +version = "0.6.0" description = "Differentiable quantum Gaussian circuits" authors = ["Xanadu "] license = "Apache License 2.0" @@ -30,15 +30,35 @@ numpy = "1.23.5" scipy = "1.8.0" numba = "0.56.4" thewalrus = "0.19.0" -tensorflow = "<=2.10.1" -tensorflow-cpu-aws = { version = "2.10.0", markers = "platform_machine == 'aarch64' or platform_machine == 'arm64'" } -tensorflow-probability = "0.18.0" rich = "10.15.1" matplotlib = "3.5.0" tqdm = "^4.65.0" ray = { version = "2.5.0", extras = ["tune"], optional = true } scikit-optimize = { version = "^0.9.0", optional = true } +###################### The Tensorflow Section ###################### +# Dedicated for making sure that poetry can install tensorflow on all platforms. +# This hopefully is a temporary workaround, pending TF starting doing a good job with their pypi release metadata. + +# Issue between poetry and tensorflow metadata since >=2.11 +# related to https://github.com/python-poetry/poetry/issues/8271 +# Inspired from https://github.com/tensorflow/tensorflow/blob/adb39b04e9cb116df4659a7e2de9eea27e62f25c/tensorflow/tools/pip_package/setup.py#L148-L162 +tensorflow = {version = "^2.14.0" } +tensorflow-macos = { version = "^2.14.0", platform = "darwin", markers = "platform_machine=='arm64'" } +tensorflow-intel = { version = "^2.14.0", platform = "win32" } +# Disabled to prevent taking over GPU: +# tensorflow-cpu = [ +# { version = "^2.14.0", platform = "linux", markers = "platform_machine!='arm64' and platform_machine!='aarch64'" }, +# { version = "^2.14.0", platform = "darwin", markers = "platform_machine!='arm64' and platform_machine!='aarch64'" },] +tensorflow-cpu-aws = { version = "^2.14.0", platform = "linux", markers = "platform_machine=='arm64' or platform_machine=='aarch64'" } +# https://github.com/tensorflow/tensorflow/blob/adb39b04e9cb116df4659a7e2de9eea27e62f25c/tensorflow/tools/pip_package/setup.py#L107-L108 +# https://github.com/python-poetry/poetry/issues/8271#issuecomment-1697740447 +tensorflow-io-gcs-filesystem = [ + { version = ">= 0.23.1", markers = "platform_machine!='arm64' or platform_system!='Darwin'" }, + { version = "< 0.32.0", markers = "platform_system == 'Windows'" } +] +tensorflow-probability = "^0.22.0" + [tool.poetry.extras] ray = ["ray", "scikit-optimize"] diff --git a/tests/random.py b/tests/random.py index 2b5f28588..df3660aae 100644 --- a/tests/random.py +++ b/tests/random.py @@ -97,6 +97,24 @@ def bounds_check(t): prob_bounds = st.tuples(none_or_(prob), none_or_(prob)).filter(bounds_check) +# settings +def force_settings(name, value): + r"""Updates the value of immutable settings. + + .. code:: + >>> settings.HBAR + 2.0 + + >>> settings.HBAR = 1.0 + ValueError: Cannot change the value of `settings.HBAR`. + + >>> settings.force_settings("_hbar", 1.0) + >>> settings.HBAR + 1.0 + """ + getattr(settings, name)._value = value # pylint: disable=protected-access + + # gates @st.composite def random_Rgate(draw, trainable=False): diff --git a/tests/test_lab/test_detectors.py b/tests/test_lab/test_detectors.py index a3e84d013..d6b4b3614 100644 --- a/tests/test_lab/test_detectors.py +++ b/tests/test_lab/test_detectors.py @@ -171,7 +171,10 @@ def test_homodyne_mode_kwargs(self, outcome): x_outcome = detector.outcome.numpy()[:2] assert np.allclose(x_outcome, outcome) - @given(s=st.floats(min_value=0.0, max_value=10.0), outcome=none_or_(st.floats(-10.0, 10.0))) + @given( + s=st.floats(min_value=0.0, max_value=10.0), + outcome=none_or_(st.floats(-10.0, 10.0)), + ) def test_homodyne_on_2mode_squeezed_vacuum(self, s, outcome): """Check that homodyne detection on TMSV for q-quadrature (``quadrature_angle=0.0``)""" r = settings.HOMODYNE_SQUEEZING @@ -195,7 +198,11 @@ def test_homodyne_on_2mode_squeezed_vacuum(self, s, outcome): ) assert np.allclose(remaining_state.means.numpy(), means) - @given(s=st.floats(1.0, 10.0), outcome=none_or_(st.floats(-2, 2)), angle=st.floats(0, np.pi)) + @given( + s=st.floats(1.0, 10.0), + outcome=none_or_(st.floats(-2, 2)), + angle=st.floats(0, np.pi), + ) def test_homodyne_on_2mode_squeezed_vacuum_with_angle(self, s, outcome, angle): """Check that homodyne detection on TMSV works with an arbitrary quadrature angle""" r = settings.HOMODYNE_SQUEEZING diff --git a/tests/test_lab/test_gates_fock.py b/tests/test_lab/test_gates_fock.py index d9799ed73..0c867f5ee 100644 --- a/tests/test_lab/test_gates_fock.py +++ b/tests/test_lab/test_gates_fock.py @@ -23,6 +23,7 @@ two_mode_squeezing, ) +from mrmustard import settings from mrmustard.lab import ( Attenuator, BSgate, @@ -34,6 +35,9 @@ Rgate, S2gate, Sgate, + Gaussian, + PhaseNoise, + Thermal, ) from mrmustard.lab.states import TMSV, Fock, SqueezedVacuum, State from mrmustard.math import Math @@ -301,6 +305,25 @@ def test_choi_cutoffs(): assert output.cutoffs == [5, 8] # cutoffs are respected by the gate +@pytest.mark.parametrize("gate", [Sgate(1), Rgate(0.1), Dgate(0.1)]) +@pytest.mark.parametrize("cutoff", [2, 5]) +@pytest.mark.parametrize("modes", [[0], [1, 2]]) +def test_choi_for_unitary(gate, cutoff, modes): + """tests the `choi` method for unitary transformations""" + gate = gate[modes] + N = gate.num_modes + cutoffs = [cutoff] * N + + choi = gate.choi(cutoffs=cutoffs).numpy().reshape(cutoff ** (2 * N), cutoff ** (2 * N)) + + t = gate.U(cutoffs=cutoffs).numpy() + row = t.flatten().reshape(1, cutoff ** (2 * N)) + col = t.flatten().reshape(cutoff ** (2 * N), 1) + expected = np.dot(col, row) + + assert np.allclose(expected, choi) + + def test_measure_with_fock(): "tests that the autocutoff respects the fock projection cutoff" cov = np.array( @@ -326,3 +349,45 @@ def test_schwinger_bs_equals_vanilla_bs_for_small_cutoffs(theta, phi): U_schwinger = BSgate(theta, phi).U([10, 10, 10, 10], method="schwinger") assert np.allclose(U_vanilla, U_schwinger, atol=1e-6) + + +# pylint: disable=protected-access +@given(phase_stdev=medium_float.filter(lambda x: x > 0)) +def test_phasenoise_creates_dm(phase_stdev): + """test that the phase noise gate is correctly applied""" + assert (Coherent(1.0) >> PhaseNoise(phase_stdev))._dm is not None + assert (Fock(10) >> PhaseNoise(phase_stdev))._dm is not None + + +@given(phase_stdev=medium_float.filter(lambda x: x > 0)) +def test_phasenoise_symmetry(phase_stdev): + "tests that symmetric states are not affected by phase noise" + assert (Fock(1) >> PhaseNoise(phase_stdev)) == Fock(1) + settings.AUTOCUTOFF_MIN_CUTOFF = 100 + assert (Thermal(1) >> PhaseNoise(phase_stdev)) == Thermal(1) + settings.AUTOCUTOFF_MIN_CUTOFF = 1 + + +@given(phase_stdev=medium_float.filter(lambda x: x > 0)) +def test_phasenoise_on_multimode(phase_stdev): + "tests that phase noise can be used on multimode states" + G2 = Gaussian(2) >> Attenuator(0.1, modes=[0, 1]) + P = PhaseNoise(phase_stdev, modes=[1]) + settings.AUTOCUTOFF_MIN_CUTOFF = 20 + assert (G2 >> P).get_modes(0) == G2.get_modes(0) + assert (G2 >> P).get_modes(1) == G2.get_modes(1) >> P + settings.AUTOCUTOFF_MIN_CUTOFF = 1 + + +def test_phasenoise_large_noise(): + "tests that large phase noise kills the off-diagonal elements" + G1 = Gaussian(1) + P = PhaseNoise(1000) + assert (G1 >> P) == State(dm=math.diag(math.diag_part(G1.dm()))) + + +def test_phasenoise_zero_noise(): + "tests that zero phase noise is equal to the identity" + G1 = Gaussian(1) + P = PhaseNoise(0.0) + assert (G1 >> P) == State(dm=G1.dm()) diff --git a/tests/test_lab/test_states.py b/tests/test_lab/test_states.py index 990699b52..45fb4a61b 100644 --- a/tests/test_lab/test_states.py +++ b/tests/test_lab/test_states.py @@ -32,9 +32,10 @@ ) from mrmustard.math import Math from mrmustard.physics import gaussian as gp -from tests.random import angle, medium_float, n_mode_pure_state, nmodes, r +from tests.random import angle, force_settings, medium_float, n_mode_pure_state, nmodes, r math = Math() +hbar0 = settings.HBAR @st.composite @@ -45,10 +46,14 @@ def xy_arrays(draw): @given(nmodes, st.floats(0.1, 5.0)) def test_vacuum_state(nmodes, hbar): - cov, disp = gp.vacuum_cov(nmodes, hbar), gp.vacuum_means(nmodes, hbar) + force_settings("_hbar", hbar) + cov, disp = gp.vacuum_cov(nmodes), gp.vacuum_means(nmodes) assert np.allclose(cov, np.eye(2 * nmodes) * hbar / 2) assert np.allclose(disp, np.zeros_like(disp)) + # restoring hbar to its original value + force_settings("_hbar", hbar0) + @given(x=medium_float, y=medium_float) def test_coherent_state_single(x, y): @@ -59,16 +64,24 @@ def test_coherent_state_single(x, y): @given(hbar=st.floats(0.5, 2.0), x=medium_float, y=medium_float) def test_coherent_state_list(hbar, x, y): - assert np.allclose(gp.displacement([x], [y], hbar), np.array([x, y]) * np.sqrt(2 * hbar)) + force_settings("_hbar", hbar) + assert np.allclose(gp.displacement([x], [y]), np.array([x, y]) * np.sqrt(2 * hbar)) + + # restoring hbar to its original value + force_settings("_hbar", hbar0) @given(hbar=st.floats(0.5, 2.0), x=medium_float, y=medium_float) def test_coherent_state_array(hbar, x, y): + force_settings("_hbar", hbar) assert np.allclose( - gp.displacement(np.array([x]), np.array([y]), hbar), + gp.displacement(np.array([x]), np.array([y])), np.array([x, y]) * np.sqrt(2 * hbar), ) + # restoring hbar to its original value + force_settings("_hbar", hbar0) + @given(xy=xy_arrays()) def test_coherent_state_multiple(xy): @@ -78,10 +91,13 @@ def test_coherent_state_multiple(xy): assert len(x) == len(y) assert np.allclose(state.means, np.concatenate([x, y], axis=-1) * np.sqrt(2 * settings.HBAR)) + # restoring hbar to its original value + force_settings("_hbar", hbar0) + @given(state=n_mode_pure_state(num_modes=1)) def test_the_purity_of_a_pure_state(state): - purity = gp.purity(state.cov, settings.HBAR) + purity = gp.purity(state.cov) expected = 1.0 assert np.isclose(purity, expected) @@ -89,7 +105,7 @@ def test_the_purity_of_a_pure_state(state): @given(nbar=st.floats(0.0, 3.0)) def test_the_purity_of_a_mixed_state(nbar): state = Thermal(nbar) - purity = gp.purity(state.cov, settings.HBAR) + purity = gp.purity(state.cov) expected = 1 / (2 * nbar + 1) assert np.isclose(purity, expected) @@ -149,9 +165,11 @@ def test_hbar(): """Test cov matrix is linear in hbar.""" g = Gaussian(2) p = g.purity - settings.HBAR = 1.234 + force_settings("_hbar", 1.234) assert g.purity == p - settings.HBAR = 2 + + # restoring hbar to its original value + force_settings("_hbar", hbar0) def test_get_single_mode(): @@ -197,12 +215,12 @@ def test_random_state_is_entangled(): """Tests that a Gaussian state generated at random is entangled.""" state = Vacuum(2) >> Ggate(num_modes=2) mat = state.cov - assert np.allclose(gp.log_negativity(mat, 2), 0.0) + assert np.allclose(gp.log_negativity(mat), 0.0) assert np.allclose( - gp.log_negativity(gp.physical_partial_transpose(mat, [0, 1]), 2), 0.0, atol=1e-7 + gp.log_negativity(gp.physical_partial_transpose(mat, [0, 1])), 0.0, atol=1e-7 ) - N1 = gp.log_negativity(gp.physical_partial_transpose(mat, [0]), 2) - N2 = gp.log_negativity(gp.physical_partial_transpose(mat, [1]), 2) + N1 = gp.log_negativity(gp.physical_partial_transpose(mat, [0])) + N2 = gp.log_negativity(gp.physical_partial_transpose(mat, [1])) assert N1 > 0 assert N2 > 0 diff --git a/tests/test_physics/test_fidelity.py b/tests/test_physics/test_fidelity.py index 3846ef4bd..722601f56 100644 --- a/tests/test_physics/test_fidelity.py +++ b/tests/test_physics/test_fidelity.py @@ -3,13 +3,15 @@ from thewalrus.quantum import real_to_complex_displacements from thewalrus.random import random_covariance -from mrmustard import physics +from mrmustard import physics, settings from mrmustard.lab import Coherent, Fock, State from mrmustard.math import Math from mrmustard.physics import fock as fp from mrmustard.physics import gaussian as gp +from tests.random import force_settings math = Math() +hbar0 = settings.HBAR class TestGaussianStates: @@ -19,78 +21,103 @@ class TestGaussianStates: @pytest.mark.parametrize("block_diag", [True, False]) def test_fidelity_is_symmetric(self, num_modes, hbar, pure, block_diag): """Test that the fidelity is symmetric""" + force_settings("_hbar", hbar) cov1 = random_covariance(num_modes, hbar=hbar, pure=pure, block_diag=block_diag) means1 = np.sqrt(2 * hbar) * np.random.rand(2 * num_modes) cov2 = random_covariance(num_modes, hbar=hbar, pure=pure, block_diag=block_diag) means2 = np.sqrt(2 * hbar) * np.random.rand(2 * num_modes) - f12 = gp.fidelity(means1, cov1, means2, cov2, hbar) - f21 = gp.fidelity(means2, cov2, means1, cov1, hbar) + f12 = gp.fidelity(means1, cov1, means2, cov2) + f21 = gp.fidelity(means2, cov2, means1, cov1) assert np.allclose(f12, f21) + # restoring hbar to its original value + force_settings("_hbar", hbar0) + @pytest.mark.parametrize("hbar", [1 / 2, 1.0, 2.0, 1.6]) @pytest.mark.parametrize("num_modes", np.arange(5, 10)) @pytest.mark.parametrize("pure", [True, False]) @pytest.mark.parametrize("block_diag", [True, False]) def test_fidelity_is_leq_one(self, num_modes, hbar, pure, block_diag): """Test that the fidelity is between 0 and 1""" + force_settings("_hbar", hbar) cov1 = random_covariance(num_modes, hbar=hbar, pure=pure, block_diag=block_diag) means1 = np.sqrt(2 * hbar) * np.random.rand(2 * num_modes) cov2 = random_covariance(num_modes, hbar=hbar, pure=pure, block_diag=block_diag) means2 = np.sqrt(2 * hbar) * np.random.rand(2 * num_modes) - f12 = gp.fidelity(means1, cov1, means2, cov2, hbar) + f12 = gp.fidelity(means1, cov1, means2, cov2) assert 0 <= np.real_if_close(f12) < 1.0 + # restoring hbar to its original value + force_settings("_hbar", hbar0) + @pytest.mark.parametrize("hbar", [1 / 2, 1.0, 2.0, 1.6]) @pytest.mark.parametrize("num_modes", np.arange(2, 6)) @pytest.mark.parametrize("pure", [True, False]) @pytest.mark.parametrize("block_diag", [True, False]) def test_fidelity_with_self(self, num_modes, hbar, pure, block_diag): """Test that the fidelity of two identical quantum states is 1""" + force_settings("_hbar", hbar) cov = random_covariance(num_modes, hbar=hbar, pure=pure, block_diag=block_diag) means = np.random.rand(2 * num_modes) - assert np.allclose(gp.fidelity(means, cov, means, cov, hbar=hbar), 1, atol=1e-3) + assert np.allclose(gp.fidelity(means, cov, means, cov), 1, atol=1e-3) + + # restoring hbar to its original value + force_settings("_hbar", hbar0) @pytest.mark.parametrize("num_modes", np.arange(5, 10)) @pytest.mark.parametrize("hbar", [0.5, 1.0, 2.0, 1.6]) def test_fidelity_coherent_state(self, num_modes, hbar): """Test the fidelity of two multimode coherent states""" + force_settings("_hbar", hbar) beta1 = np.random.rand(num_modes) + 1j * np.random.rand(num_modes) beta2 = np.random.rand(num_modes) + 1j * np.random.rand(num_modes) means1 = real_to_complex_displacements(np.concatenate([beta1, beta1.conj()]), hbar=hbar) means2 = real_to_complex_displacements(np.concatenate([beta2, beta2.conj()]), hbar=hbar) cov1 = hbar * np.identity(2 * num_modes) / 2 cov2 = hbar * np.identity(2 * num_modes) / 2 - fid = gp.fidelity(means1, cov1, means2, cov2, hbar=hbar) + fid = gp.fidelity(means1, cov1, means2, cov2) expected = np.exp(-np.linalg.norm(beta1 - beta2) ** 2) assert np.allclose(expected, fid) + # restoring hbar to its original value + force_settings("_hbar", hbar0) + @pytest.mark.parametrize("hbar", [0.5, 1.0, 2.0, 1.6]) @pytest.mark.parametrize("r1", np.random.rand(3)) @pytest.mark.parametrize("r2", np.random.rand(3)) def test_fidelity_squeezed_vacuum(self, r1, r2, hbar): """Tests fidelity between two squeezed states""" + force_settings("_hbar", hbar) cov1 = np.diag([np.exp(2 * r1), np.exp(-2 * r1)]) * hbar / 2 cov2 = np.diag([np.exp(2 * r2), np.exp(-2 * r2)]) * hbar / 2 mu = np.zeros([2]) - assert np.allclose(1 / np.cosh(r1 - r2), gp.fidelity(mu, cov1, mu, cov2, hbar=hbar)) + assert np.allclose(1 / np.cosh(r1 - r2), gp.fidelity(mu, cov1, mu, cov2)) + + # restoring hbar to its original value + force_settings("_hbar", hbar0) @pytest.mark.parametrize("n1", [0.5, 1.0, 2.0, 1.6]) @pytest.mark.parametrize("n2", [0.5, 1.0, 2.0, 1.6]) @pytest.mark.parametrize("hbar", [0.5, 1.0, 2.0, 1.6]) def test_fidelity_thermal(self, n1, n2, hbar): """Test fidelity between two thermal states""" + force_settings("_hbar", hbar) expected = 1 / (1 + n1 + n2 + 2 * n1 * n2 - 2 * np.sqrt(n1 * n2 * (n1 + 1) * (n2 + 1))) cov1 = hbar * (n1 + 0.5) * np.identity(2) cov2 = hbar * (n2 + 0.5) * np.identity(2) mu1 = np.zeros([2]) mu2 = np.zeros([2]) - assert np.allclose(expected, gp.fidelity(mu1, cov1, mu2, cov2, hbar=hbar)) + assert np.allclose(expected, gp.fidelity(mu1, cov1, mu2, cov2)) + + # restoring hbar to its original value + force_settings("_hbar", hbar0) @pytest.mark.parametrize("hbar", [0.5, 1.0, 2.0, 1.6]) @pytest.mark.parametrize("r", [-2.0, 0.0, 2.0]) @pytest.mark.parametrize("alpha", np.random.rand(10) + 1j * np.random.rand(10)) def test_fidelity_vac_to_displaced_squeezed(self, r, alpha, hbar): """Calculates the fidelity between a coherent squeezed state and vacuum""" + force_settings("_hbar", hbar) cov1 = np.diag([np.exp(2 * r), np.exp(-2 * r)]) * hbar / 2 means1 = real_to_complex_displacements(np.array([alpha, np.conj(alpha)]), hbar=hbar) means2 = np.zeros([2]) @@ -100,7 +127,10 @@ def test_fidelity_vac_to_displaced_squeezed(self, r, alpha, hbar): * np.abs(np.exp(np.tanh(r) * np.conj(alpha) ** 2)) / np.cosh(r) ) - assert np.allclose(expected, gp.fidelity(means1, cov1, means2, cov2, hbar=hbar)) + assert np.allclose(expected, gp.fidelity(means1, cov1, means2, cov2)) + + # restoring hbar to its original value + force_settings("_hbar", hbar0) class TestMixedStates: diff --git a/tests/test_physics/test_fock/test_fock.py b/tests/test_physics/test_fock/test_fock.py index 8c673aee8..eeb94e568 100644 --- a/tests/test_physics/test_fock/test_fock.py +++ b/tests/test_physics/test_fock/test_fock.py @@ -252,6 +252,14 @@ def test_apply_kraus_to_ket_1mode(): assert ket_out.shape == (2, 5, 4) +def test_apply_kraus_to_ket_1mode_with_argument_names(): + """Test that Kraus operators are applied to a ket on the correct indices with argument names""" + ket = np.random.normal(size=(2, 3, 4)) + kraus = np.random.normal(size=(5, 3)) + ket_out = fock.apply_kraus_to_ket(kraus=kraus, ket=ket, kraus_in_modes=[1], kraus_out_modes=[1]) + assert ket_out.shape == (2, 5, 4) + + def test_apply_kraus_to_ket_2mode(): """Test that Kraus operators are applied to a ket on the correct indices""" ket = np.random.normal(size=(2, 3, 4)) @@ -276,6 +284,14 @@ def test_apply_kraus_to_dm_1mode(): assert dm_out.shape == (2, 5, 2, 5) +def test_apply_kraus_to_dm_1mode_with_argument_names(): + """Test that Kraus operators are applied to a dm on the correct indices with argument names""" + dm = np.random.normal(size=(2, 3, 2, 3)) + kraus = np.random.normal(size=(5, 3)) + dm_out = fock.apply_kraus_to_dm(kraus=kraus, dm=dm, kraus_in_modes=[1], kraus_out_modes=[1]) + assert dm_out.shape == (2, 5, 2, 5) + + def test_apply_kraus_to_dm_2mode(): """Test that Kraus operators are applied to a dm on the correct indices""" dm = np.random.normal(size=(2, 3, 4, 2, 3, 4)) @@ -300,6 +316,14 @@ def test_apply_choi_to_ket_1mode(): assert ket_out.shape == (4, 5, 4, 5) +def test_apply_choi_to_ket_1mode_with_argument_names(): + """Test that choi operators are applied to a ket on the correct indices with argument names""" + ket = np.random.normal(size=(3, 5)) + choi = np.random.normal(size=(4, 3, 4, 3)) # [out_l, in_l, out_r, in_r] + ket_out = fock.apply_choi_to_ket(choi=choi, ket=ket, choi_in_modes=[0], choi_out_modes=[0]) + assert ket_out.shape == (4, 5, 4, 5) + + def test_apply_choi_to_ket_2mode(): """Test that choi operators are applied to a ket on the correct indices""" ket = np.random.normal(size=(3, 5)) @@ -316,6 +340,14 @@ def test_apply_choi_to_dm_1mode(): assert dm_out.shape == (4, 5, 4, 5) +def test_apply_choi_to_dm_1mode_with_argument_names(): + """Test that choi operators are applied to a dm on the correct indices with argument names""" + dm = np.random.normal(size=(3, 5, 3, 5)) + choi = np.random.normal(size=(4, 3, 4, 3)) # [out_l, in_l, out_r, in_r] + dm_out = fock.apply_choi_to_dm(choi=choi, dm=dm, choi_in_modes=[0], choi_out_modes=[0]) + assert dm_out.shape == (4, 5, 4, 5) + + def test_apply_choi_to_dm_2mode(): """Test that choi operators are applied to a dm on the correct indices""" dm = np.random.normal(size=(4, 5, 4, 5)) diff --git a/tests/test_physics/test_gaussian/test_symplectics.py b/tests/test_physics/test_gaussian/test_symplectics.py index 832f89cc3..7843eb8f6 100644 --- a/tests/test_physics/test_gaussian/test_symplectics.py +++ b/tests/test_physics/test_gaussian/test_symplectics.py @@ -15,7 +15,13 @@ import numpy as np from hypothesis import given from hypothesis import strategies as st -from thewalrus.symplectic import beam_splitter, expand, rotation, squeezing, two_mode_squeezing +from thewalrus.symplectic import ( + beam_splitter, + expand, + rotation, + squeezing, + two_mode_squeezing, +) from mrmustard.lab import ( Amplifier, diff --git a/tests/test_settings.py b/tests/test_settings.py index e1e93e552..bae36f9d0 100644 --- a/tests/test_settings.py +++ b/tests/test_settings.py @@ -16,24 +16,70 @@ Tests for the Settings class. """ -from mrmustard import Settings - - -def test_settings_seed_randomness_at_init(): - """Test that the random seed is set randomly as MM is initialized.""" - settings = Settings() - seed0 = settings.SEED - del Settings.instance - settings = Settings() - seed1 = settings.SEED - assert seed0 != seed1 - - -def test_reproducibility(): - """Test that the random state is reproducible.""" - settings = Settings() - settings.SEED = 42 - seq0 = [settings.rng.integers(0, 2**31 - 1) for _ in range(10)] - settings.SEED = 42 - seq1 = [settings.rng.integers(0, 2**31 - 1) for _ in range(10)] - assert seq0 == seq1 +from mrmustard.settings import Settings, ImmutableSetting +import pytest + + +class TestImmutableSettings: + """Tests the ImmutableSettings class""" + + def test_init(self): + """Tests the default values of the immutable settings""" + s = ImmutableSetting("foo", "bar") + assert s.value == "foo" + assert s.name == "bar" + + def test_setting_becomes_immutable(self): + """Tests that immutable settings become immutable""" + s = ImmutableSetting(1, "my_name") + + s.value = 2 + assert s.value == 2 + + with pytest.raises(ValueError, match=f"value of `settings.{s.name}`"): + s.value = 3 + + +class TestSettings: + """Tests the Settings class""" + + def test_init(self): + """Test the default values of the settings""" + settings = Settings() + + assert settings.BACKEND == "tensorflow" + assert settings.HBAR == 2.0 + assert settings.DEBUG is False + assert settings.AUTOCUTOFF_PROBABILITY == 0.999 # capture at least 99.9% of the probability + assert settings.AUTOCUTOFF_MAX_CUTOFF == 100 + assert settings.AUTOCUTOFF_MIN_CUTOFF == 1 + assert settings.CIRCUIT_DECIMALS == 3 + assert settings.DISCRETIZATION_METHOD == "iterative" + assert settings.EQ_TRANSFORMATION_CUTOFF == 3 + assert settings.EQ_TRANSFORMATION_RTOL_FOCK == 1e-3 + assert settings.EQ_TRANSFORMATION_RTOL_GAUSS == 1e-6 + assert settings.PNR_INTERNAL_CUTOFF == 50 + assert settings.HOMODYNE_SQUEEZING == 10.0 + assert settings.PROGRESSBAR is True + assert settings.DEFAULT_BS_METHOD == "vanilla" # can be 'vanilla' or 'schwinger' + + with pytest.raises(ValueError, match="Cannot change"): + settings.HBAR = 3 + + def test_settings_seed_randomness_at_init(self): + """Test that the random seed is set randomly as MM is initialized.""" + settings = Settings() + seed0 = settings.SEED + del Settings.instance + settings = Settings() + seed1 = settings.SEED + assert seed0 != seed1 + + def test_reproducibility(self): + """Test that the random state is reproducible.""" + settings = Settings() + settings.SEED = 42 + seq0 = [settings.rng.integers(0, 2**31 - 1) for _ in range(10)] + settings.SEED = 42 + seq1 = [settings.rng.integers(0, 2**31 - 1) for _ in range(10)] + assert seq0 == seq1 diff --git a/tests/test_training/test_opt.py b/tests/test_training/test_opt.py index 9db5b0797..40adfce16 100644 --- a/tests/test_training/test_opt.py +++ b/tests/test_training/test_opt.py @@ -31,7 +31,13 @@ S2gate, Sgate, ) -from mrmustard.lab.states import DisplacedSqueezed, Fock, Gaussian, SqueezedVacuum, Vacuum +from mrmustard.lab.states import ( + DisplacedSqueezed, + Fock, + Gaussian, + SqueezedVacuum, + Vacuum, +) from mrmustard.math import Math from mrmustard.physics import fidelity from mrmustard.physics.gaussian import trace, von_neumann_entropy @@ -368,7 +374,7 @@ def cost_fn(): cov1, _ = trace(state.cov, state.means, [0]) mean1 = state.number_means[0] mean2 = state.number_means[1] - entropy = von_neumann_entropy(cov1, settings.HBAR) + entropy = von_neumann_entropy(cov1) S = thermal_entropy(nbar) return (mean1 - nbar) ** 2 + (entropy - S) ** 2 + (mean2 - nbar) ** 2 diff --git a/tests/test_training/test_trainer.py b/tests/test_training/test_trainer.py index 1ac08eb9e..5c033dd82 100644 --- a/tests/test_training/test_trainer.py +++ b/tests/test_training/test_trainer.py @@ -90,7 +90,7 @@ def test_circ_cost(self, wrappers, tasks, seed): # pylint: disable=redefined-ou @pytest.mark.parametrize( "tasks", - [[{"x": 0.1}, {"y_targ": 0.2}], {"c0": {}, "c1": {"euclidean_lr": 0.02, "HBAR": 1.0}}], + [[{"x": 0.1}, {"y_targ": 0.2}], {"c0": {}, "c1": {"euclidean_lr": 0.02}}], ) @pytest.mark.parametrize( "return_type", diff --git a/tests/test_utils/test_wigner.py b/tests/test_utils/test_wigner.py index 60395d3b8..912e6729a 100644 --- a/tests/test_utils/test_wigner.py +++ b/tests/test_utils/test_wigner.py @@ -16,109 +16,218 @@ import numpy as np import pytest -from scipy.stats import multivariate_normal +from scipy.special import assoc_laguerre from mrmustard import settings from mrmustard.lab import ( Coherent, - DisplacedSqueezed, Fock, - Gaussian, SqueezedVacuum, - Thermal, - Vacuum, + State, ) from mrmustard.utils.wigner import wigner_discretized +from tests.random import force_settings +# original settings +autocutoff_max0 = settings.AUTOCUTOFF_MAX_CUTOFF +autocutoff_min0 = settings.AUTOCUTOFF_MIN_CUTOFF +method0 = settings.DISCRETIZATION_METHOD +hbar0 = settings.HBAR -def multivariate_normal_pdf(qvec, pvec, means, cov): - """generates the PDF of a multivariate normal distribution""" - mvn = multivariate_normal(means, cov, allow_singular=True) - grid = np.meshgrid(qvec, pvec) - return mvn.pdf(grid) +# ~~~~~~~ +# Helpers +# ~~~~~~~ -@pytest.mark.parametrize( - "state", - [ - Vacuum(1), - Coherent(0.3, -0.5), - SqueezedVacuum(0.5, 0.45), - Thermal(0.25), - DisplacedSqueezed(0.3, 0.1, -0.1, 0.1), - Gaussian(1), - ], -) -def test_wigner_gaussian_states(state): - """test Wigner function for Gaussian states is a standard normal distribution""" +def reset_settings(): + r"""Resets `Settings`""" + settings.AUTOCUTOFF_MAX_CUTOFF = autocutoff_max0 + settings.AUTOCUTOFF_MIN_CUTOFF = autocutoff_min0 + settings.DISCRETIZATION_METHOD = method0 + force_settings("_hbar", hbar0) - # calculate Wigner from state dm - qvec = np.arange(-5, 5, 100) - pvec = qvec - dm = state.dm(cutoffs=[5]).numpy() - W_calc, _, _ = wigner_discretized(dm, qvec, pvec) - # calculate exact - cov = state.cov.numpy() - means = state.means.numpy() - W_exact = multivariate_normal_pdf(qvec, pvec, means, cov) +def distance(W_mm, W_th): + r"""Calculates the distance between the discretized Wigner functions W_mm (generated + by `mrmustard`) and W_th (computed analytically) as the maximum of `|W_mm-W_th|/|W_th|`, + where . + """ + num = np.abs(W_mm - W_th) + den = np.abs(W_th) + return (num / den).max() - assert np.allclose(W_calc, W_exact, atol=0.001, rtol=0) +def W_cat(q_vec, p_vec, q0): + r"""Calculates the discretized Wigner function for a cat state with + coherent states centered in `(q0, 0)`. See Eq. 3.3 in arXiv:0406015. + """ -# Exact marginal probability distributions for various states -hbar = settings.HBAR + def generator(q, p, q0): + norm = (1 + np.exp(-(q0**2))) ** -0.5 + W_plus = np.exp(-((q + q0) ** 2) - p**2) + W_minus = np.exp(-((q - q0) ** 2) - p**2) + W_int = np.cos(2 * p * q0) * np.exp(-(q**2) - p**2) + return (W_plus / 2 + W_minus / 2 + W_int) * norm**2 / np.pi / settings.HBAR + q = q_vec / (settings.HBAR) ** 0.5 + p = p_vec / (settings.HBAR) ** 0.5 -def fock1_marginal(qvec): - """q and p marginal distributions for the Fock state |1>""" - x = ( - 0.5 - * np.sqrt(1 / (np.pi * hbar)) - * np.exp(-1 * (qvec**2) / hbar) - * (4 / hbar) - * (qvec**2) - ) - p = x - return x, p + return np.array([[generator(i, j, q0 * 2**0.5) for j in p] for i in q]) -def vacuum_marginal(qvec): - """q and p marginal distributions for the vacuum state""" - x = np.sqrt(1 / (np.pi * hbar)) * np.exp(-1 * (qvec**2) / hbar) - p = x - return x, p +def W_coherent(q_vec, p_vec, alpha, s): + r"""Calculates the discretized Wigner function for a coherent state centered + around `alpha` and with squeezing `s`. See Eq. 4.12 in arXiv:0406015. + """ + def generator(q, p, alpha, s): + q0 = np.real(alpha) * 2**0.5 + p0 = np.imag(alpha) * 2**0.5 + ret = -np.exp(2 * s) * (q - q0) ** 2 - np.exp(-2 * s) * (p - p0) ** 2 + return np.exp(ret) / np.pi / settings.HBAR -def coherent_marginal(qvec): - r"""q and p marginal distributions for the coherent state with `\alpha=1`""" - x = np.sqrt(1 / (np.pi * hbar)) * np.exp(-1 * ((qvec - 0.5 * np.sqrt(2 * hbar)) ** 2) / hbar) - p = np.sqrt(1 / (np.pi * hbar)) * np.exp(-1 * (qvec**2) / hbar) - return x, p + q = q_vec / (settings.HBAR) ** 0.5 + p = p_vec / (settings.HBAR) ** 0.5 + return np.array([[generator(i, j, alpha, s) for j in p] for i in q]) -@pytest.mark.parametrize( - "state, f_marginal", - [ - (Vacuum(1), vacuum_marginal), - (Coherent(1.0, 0.0), coherent_marginal), - (Fock([1]), fock1_marginal), - ], -) -def test_marginal_wigner(state, f_marginal): - """test marginals of Wigner function agree with the expected ones""" - # calculate Wigner from state dm - qvec = np.arange(-5, 5, 100) - pvec = qvec - dm = state.dm(cutoffs=[5]).numpy() - W_calc, _, _ = wigner_discretized(dm, qvec, pvec) +def W_fock(q_vec, p_vec, n): + r"""Calculates the discretized Wigner function for a fock state. + See Eq. 4.10 in arXiv:0406015. + """ + + def generator(q, p, n): + alpha2 = q**2 + p**2 + ret = (-1) ** n * np.exp(-alpha2) * assoc_laguerre(2 * alpha2, n) + return ret / np.pi / settings.HBAR + + q = q_vec / (settings.HBAR) ** 0.5 + p = p_vec / (settings.HBAR) ** 0.5 + + return np.array([[generator(i, j, n) for j in p] for i in q]) + + +# ~~~~~ +# Tests +# ~~~~~ + + +class TestWignerDiscretized: + r"""Tests discretized Wigner functions (DWF) for various states""" + + @pytest.mark.parametrize("method", ["iterative", "clenshaw"]) + @pytest.mark.parametrize("hbar", [1, 2]) + def test_cat_state(self, method, hbar): + r"""Tests DWF for cat states""" + settings.DISCRETIZATION_METHOD = method + force_settings("_hbar", hbar) + + q_vec = np.linspace(-4, 4, 100) + p_vec = np.linspace(-1.5, 1.5, 100) + + q0 = 2.0 + cat_amps = Coherent(q0).ket([20]) + Coherent(-q0).ket([20]) + cat_amps = cat_amps / np.linalg.norm(cat_amps) + state = State(ket=cat_amps) + W_mm, q_mat, p_mat = wigner_discretized(state.dm(), q_vec, p_vec) + W_th = W_cat(q_vec, p_vec, q0) + + assert np.allclose(distance(W_mm, W_th), 0, atol=10**-1) + assert np.allclose(q_mat.T, q_vec) + assert np.allclose(p_mat, p_vec) + + reset_settings() + + @pytest.mark.parametrize("alpha", [0 + 0j, 3 + 3j]) + @pytest.mark.parametrize("hbar", [2, 3]) + @pytest.mark.parametrize("method", ["iterative", "clenshaw"]) + def test_coherent_state(self, alpha, hbar, method): + r"""Tests DWF for coherent states""" + settings.AUTOCUTOFF_MIN_CUTOFF = 100 + settings.AUTOCUTOFF_MAX_CUTOFF = 150 + settings.DISCRETIZATION_METHOD = method + force_settings("_hbar", hbar) + + # centering the intervals around alpha--away from the center, + # the values are small and unstable. + left = (np.real(alpha) * 2**0.5 - 1) * (settings.HBAR) ** 0.5 + right = (np.real(alpha) * 2**0.5 + 1) * (settings.HBAR) ** 0.5 + q_vec = np.linspace(left, right, 50) + p_vec = np.linspace(left, right, 50) + + state = Coherent(np.real(alpha), np.imag(alpha)) + W_mm, q_mat, p_mat = wigner_discretized(state.dm(), q_vec, p_vec) + W_th = W_coherent(q_vec, p_vec, alpha, 0) + + assert np.allclose(distance(W_mm, W_th), 0) + assert np.allclose(q_mat.T, q_vec) + assert np.allclose(p_mat, p_vec) + + reset_settings() - # calculate marginals - q_marginal = np.sum(W_calc, axis=1) - p_marginal = np.sum(W_calc, axis=0) + @pytest.mark.parametrize("n", [2, 6]) + @pytest.mark.parametrize("hbar", [2, 3]) + @pytest.mark.parametrize("method", ["iterative", "clenshaw"]) + def test_fock_state(self, n, hbar, method): + r"""Tests DWF for fock states""" + settings.DISCRETIZATION_METHOD = method + force_settings("_hbar", hbar) - expected_q_marginal, expected_p_marginal = f_marginal(qvec) + q_vec = np.linspace(-1, 1, 20) + p_vec = np.linspace(-1, 1, 20) + + state = Fock(n) + W_mm, q_mat, p_mat = wigner_discretized(state.dm(), q_vec, p_vec) + W_th = W_fock(q_vec, p_vec, n) + + assert np.allclose(distance(W_mm, W_th), 0) + assert np.allclose(q_mat.T, q_vec) + assert np.allclose(p_mat, p_vec) - assert np.allclose(q_marginal, expected_q_marginal, atol=0.001, rtol=0) - assert np.allclose(p_marginal, expected_p_marginal, atol=0.001, rtol=0) + reset_settings() + + @pytest.mark.parametrize("method", ["iterative", "clenshaw"]) + def test_squeezed_vacuum_both_method_succeed(self, method): + r"""Tests DWF for a squeezed vacuum state with squeezing s=1. + Both discretization methods are expected to pass successfully. + """ + settings.AUTOCUTOFF_MIN_CUTOFF = 100 + settings.AUTOCUTOFF_MAX_CUTOFF = 150 + settings.DISCRETIZATION_METHOD = method + + q_vec = np.linspace(-0.5, 0.5, 50) + p_vec = np.linspace(-5, 5, 50) + + s = 1 + state = SqueezedVacuum(s) + W_mm, q_mat, p_mat = wigner_discretized(state.dm(), q_vec, p_vec) + W_th = W_coherent(q_vec, p_vec, 0j, s) + + assert np.allclose(distance(W_mm, W_th), 0, atol=10**-1) + assert np.allclose(q_mat.T, q_vec) + assert np.allclose(p_mat, p_vec) + + reset_settings() + + @pytest.mark.parametrize("method", ["iterative", "clenshaw"]) + def test_squeezed_vacuum_iterative_fails(self, method): + r"""Tests DWF for a squeezed vacuum state with squeezing s=2. + The iterative method cannot produce a DWF that matched with the analytical one. + """ + settings.AUTOCUTOFF_MIN_CUTOFF = 100 + settings.AUTOCUTOFF_MAX_CUTOFF = 150 + settings.DISCRETIZATION_METHOD = method + + q_vec = np.linspace(-0.2, 0.2, 50) + p_vec = np.linspace(-5, 5, 50) + + s = 2 + state = SqueezedVacuum(s) + W_mm, _, _ = wigner_discretized(state.dm(), q_vec, p_vec) + W_th = W_coherent(q_vec, p_vec, 0j, s) + + success = np.allclose(distance(W_mm, W_th), 0, atol=10**-1) + assert success is False if method == "iterative" else True + + reset_settings()