<gh_stars>0
# Copyright 2019 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.

"""
Fock backend proper
======================

Contains most of the code for managing the simulator state and offloading
operations to the utilities in ops.

Hyperlinks: :class:`Circuit`

.. currentmodule:: strawberryfields.backends.fockbackend.circuit

Contents
----------------------
.. autosummary::
     Circuit

"""
# pylint: disable=too-many-arguments,len-as-condition,attribute-defined-outside-init
# pylint: disable=too-many-branches,too-many-locals,too-many-public-methods

import copy
import string
import numbers
from itertools import product

import numpy as np
from numpy import sqrt, pi
from scipy.special import factorial as bang

from . import ops

indices = string.ascii_lowercase
MAX_MODES = len(indices) - 3

class Circuit():
    """
    Class implementing a basic simulator for a collection of modes
    in the fock basis.
    """

    def __init__(self, num, trunc, hbar=2, pure=True, do_checks=False, mode='blas'):
        r"""Class initializer.

        Args:
            num (non-negative int): Number of modes in the register.
            trunc (positive int): Truncation parameter.  Fock states up to |trunc-1> are representable.
            hbar (int): The value of :math:`\hbar` to initialise the circuit with, depending on the conventions followed.
                By default, :math:`\hbar=2`. See :ref:`conventions` for more details.
            pure (bool, optional): Whether states are pure (True) or mixed (False)
            do_checks (bool, optional): Whether arguments are to be checked first
            mode (str, optional): Whether to use BLAS or einsum for matrix operations.
        """

        # Check validity
        if num < 0:
            raise ValueError("Number of modes must be non-negative -- got {}".format(num))
        if num > MAX_MODES:
            raise ValueError("Fock simulator has a maximum of {} modes".format(MAX_MODES))
        if trunc <= 0:
            raise ValueError("Truncation must be positive -- got {}".format(trunc))

        self._num_modes = num
        self._hbar = hbar
        self._checks = do_checks
        self._mode = mode
        self.reset(pure=pure, cutoff_dim=trunc)

    def _apply_gate(self, mat, modes):
        """Master gate application function. Selects between implementations based
        on the `_mode` class parameter.

        Args:
            mat (array): The matrix to apply
            modes (list<non-negative int>): The modes to apply `mat` to
        """

        args = [mat, self._state, self._pure, modes, self._num_modes, self._trunc]
        if self._mode == 'blas':
            self._state = ops.apply_gate_BLAS(*args)
        elif self._mode == 'einsum':
            self._state = ops.apply_gate_einsum(*args)
        else:
            raise NotImplementedError

    def _apply_channel(self, kraus_ops, modes):
        """Master channel application function. Applies a channel represented by
        Kraus operators.

        .. note::
                Always results in a mixed state.

        Args:
            kraus_ops (list<array>): A list of Kraus operators
            modes (list<non-negative int>): The modes to apply the channel to
        """

        if self._pure:
            self._state = ops.mix(self._state, self._num_modes)
            self._pure = False

        if len(kraus_ops) == 0:
            self._state = np.zeros([self._trunc for i in range(self._num_modes*2)], dtype=ops.def_type)
        elif self._mode == 'blas':
            states = [ops.apply_gate_einsum(k, np.copy(self._state), False, modes, self._num_modes, self._trunc)\
                                    for k in kraus_ops]
            self._state = sum(states)
        elif self._mode == 'einsum':
            states = [ops.apply_gate_einsum(k, self._state, False, modes, self._num_modes, self._trunc)\
                                    for k in kraus_ops]
            self._state = sum(states)


    def reset(self, pure=None, cutoff_dim=None, num_subsystems=None):
        """Resets the simulation state.

        Args:
            pure (bool, optional): Sets the purity setting. Default is unchanged.
            cutoff_dim (int): New Hilbert space truncation dimension.
            num_subsystems (int, optional): Sets the number of modes in the reset
                circuit. Default is unchanged.
        """
        if pure is not None:
            if not isinstance(pure, bool):
                raise ValueError("Argument 'pure' must be either True or False")
            self._pure = pure

        if num_subsystems is not None:
            if not isinstance(num_subsystems, int):
                raise ValueError("Argument 'num_subsystems' must be a positive integer")
            self._num_modes = num_subsystems

        if cutoff_dim is not None:
            if not isinstance(cutoff_dim, int) or cutoff_dim < 1:
                raise ValueError("Argument 'cutoff_dim' must be a positive integer")
            self._trunc = cutoff_dim

        if self._pure:
            self._state = ops.vacuumState(self._num_modes, self._trunc)
        else:
            self._state = ops.vacuumStateMixed(self._num_modes, self._trunc)

    def norm(self):
        """returns the norm of the state"""
        if self._pure:
            return sqrt(np.vdot(self._state, self._state).real)
        return ops.trace(self._state, self._num_modes)

    def alloc(self, n=1):
        """allocate a number of modes at the end of the state."""
        # base_shape = [self._trunc for i in range(n)]
        if self._pure:
            vac = ops.vacuumState(n, self._trunc)
        else:
            vac = ops.vacuumStateMixed(n, self._trunc)

        self._state = ops.tensor(self._state, vac, self._num_modes, self._pure)
        self._num_modes = self._num_modes + n

    def dealloc(self, modes):
        """Traces out and deallocates the modes in `modes`"""
        if self._pure:
            self._state = ops.mix(self._state, self._num_modes)
            self._pure = False

        self._state = ops.partial_trace(self._state, self._num_modes, modes)
        self._num_modes = self._num_modes - len(modes)

    def prepare_multimode(self, state, modes):
        r"""
        Prepares a given mode or list of modes in the given state.

        After the preparation the system is in a mixed product state,
        with the specified modes replaced by state.
        The given state can be either in tensor form or in matrix/vector form.
        If modes is not ordered, the subsystems of the input are
        reordered to reflect that, i.e., if modes=[3,1], then the first mode
        of state ends up in mode 3 and the second mode of state ends up in
        mode 1 of the output state.
        If modes is None, it is attempted to prepare state in all modes.
        The reduced state on all other modes remains unchanged and
        the final state is product with respect to the partition into
        the modes in modes and the complement.

        Args:
            state (array): vector, matrix, or tensor representation of the ket state or dm state in the fock basis to prepare
            modes (list[int] or non-negative int): The mode(s) into which state is to be prepared. Needs not be ordered.
        """
        if isinstance(modes, int):
            modes = [modes]

        n_modes = len(modes)
        pure_shape = tuple([self._trunc]*n_modes)
        mixed_shape = tuple([self._trunc]*(2*n_modes))
        pure_shape_as_vector = tuple([self._trunc**n_modes])
        mixed_shape_as_matrix = tuple([self._trunc**n_modes]*2)

        # Do consistency checks
        if self._checks:
            if state.shape != pure_shape and state.shape != mixed_shape \
               and \
               state.shape != pure_shape_as_vector and state.shape != mixed_shape_as_matrix:
                raise ValueError("Incorrect shape for state preparation")
            if len(modes) != len(set(modes)):
                raise ValueError("The specified modes cannot appear multiple times.")

        # reshape to support input both as tensor and vector/matrix
        if state.shape == pure_shape_as_vector:
            state = state.reshape(pure_shape)
        elif state.shape == mixed_shape_as_matrix:
            state = state.reshape(mixed_shape)

        if self._num_modes == n_modes:
            # Hack for marginally faster state preparation
            self._state = state.astype(ops.def_type)
            self._pure = bool(state.shape == pure_shape)
        else:
            if self._pure:
                self._state = ops.mix(self._state, self._num_modes)
                self._pure = False

            if state.shape == pure_shape:
                state = ops.mix(state, len(modes))

            # Take the partial trace
            # todo: For performance the partial trace could be done directly from the pure state. This would of course require a better partial trace function...
            reduced_state = ops.partial_trace(self._state, self._num_modes, modes)

            # Insert state at the end (I know there is also tensor() from ops but it has extra aguments wich only confuse here)
            self._state = np.tensordot(reduced_state, state, axes=0)

            # unless the preparation was meant to go into the last modes in the standard order, we need to swap indices around
        if modes != list(range(self._num_modes-len(modes), self._num_modes)):
            mode_permutation = [x for x in range(self._num_modes) if x not in modes] + modes
            if self._pure:
                scale = 1
                index_permutation = mode_permutation
            else:
                scale = 2
                index_permutation = [scale*x+i for x in mode_permutation for i in (0, 1)] #two indices per mode if we have pure states
            index_permutation = np.argsort(index_permutation)

            self._state = np.transpose(self._state, index_permutation)

    def prepare(self, state, mode):
        r"""
        Prepares a given mode in a given state.

        This is a simple wrapper for prepare_multimode(), see there for more details.

        Args:
            state (array): vector, matrix, or tensor representation of the ket state or dm state in the fock basis to prepare
            modes (list[int] or non-negative int or None): The mode(s) into which state is to be prepared. Needs not be ordered.
        """
        if isinstance(mode, int):
            mode = [mode]
        self.prepare_multimode(state, mode)

    def prepare_mode_fock(self, n, mode):
        """
        Prepares a mode in a fock state.
        """

        if self._pure:
            self.prepare(ops.fockState(n, self._trunc), mode)
        else:
            st = ops.fockState(n, self._trunc)
            self.prepare(np.outer(st, st.conjugate()), mode)

    def prepare_mode_coherent(self, alpha, mode):
        """
        Prepares a mode in a coherent state.
        """
        if self._pure:
            self.prepare(ops.coherentState(alpha, self._trunc), mode)
        else:
            st = ops.coherentState(alpha, self._trunc)
            self.prepare(np.outer(st, st.conjugate()), mode)

    def prepare_mode_squeezed(self, r, theta, mode):
        """
        Prepares a mode in a squeezed state.
        """
        if self._pure:
            self.prepare(ops.squeezedState(r, theta, self._trunc), mode)
        else:
            st = ops.squeezedState(r, theta, self._trunc)
            self.prepare(np.outer(st, st.conjugate()), mode)

    def prepare_mode_displaced_squeezed(self, alpha, r, phi, mode):
        """
        Prepares a mode in a displaced squeezed state.
        """
        if self._pure:
            self.prepare(ops.displacedSqueezed(alpha, r, phi, self._trunc), mode)
        else:
            st = ops.displacedSqueezed(alpha, r, phi, self._trunc)
            self.prepare(np.outer(st, st.conjugate()), mode)

    def prepare_mode_thermal(self, nbar, mode):
        """
        Prepares a mode in a thermal state.
        """
        st = ops.thermalState(nbar, self._trunc)
        self.prepare(st, mode)

    def phase_shift(self, theta, mode):
        """
        Applies a phase shifter.
        """
        self._apply_gate(ops.phase(theta, self._trunc), [mode])

    def displacement(self, alpha, mode):
        """
        Applies a displacement gate.
        """
        self._apply_gate(ops.displacement(alpha, self._trunc), [mode])

    def beamsplitter(self, t, r, phi, mode1, mode2):
        """
        Applies a beamsplitter.
        """
        self._apply_gate(ops.beamsplitter(t, r, phi, self._trunc), [mode1, mode2])

    def squeeze(self, r, theta, mode):
        """
        Applies a squeezing gate.
        """
        self._apply_gate(ops.squeezing(r, theta, self._trunc), [mode])

    def kerr_interaction(self, kappa, mode):
        """
        Applies a Kerr interaction gate.
        """
        self._apply_gate(ops.kerr(kappa, self._trunc), [mode])

    def cross_kerr_interaction(self, kappa, mode1, mode2):
        """
        Applies a cross-Kerr interaction gate.
        """
        self._apply_gate(ops.cross_kerr(kappa, self._trunc), [mode1, mode2])

    def cubic_phase_shift(self, gamma, mode):
        """
        Applies a cubic phase shift gate.
        """
        self._apply_gate(ops.cubicPhase(gamma, self._hbar, self._trunc), [mode])

    def is_vacuum(self, tol):
        """
        Tests whether the system is in the vacuum state.
        """
        # base_shape = [self._trunc for i in range(self._num_modes)]
        if self._pure:
            vac = ops.vacuumState(self._num_modes, self._trunc)
        else:
            vac = ops.vacuumStateMixed(self._num_modes, self._trunc)
        return np.linalg.norm((self._state - vac).ravel()) < tol

    def get_state(self):
        """
        Returns the state of the system in the fock basis along with its purity.
        """
        return self._state, self._pure

    def loss(self, T, mode):
        """
        Applies a loss channel to the state.
        """
        self._apply_channel(ops.lossChannel(T, self._trunc), [mode])

    def measure_fock(self, modes, select=None):
        """
        Measures a list of modes.
        """
        # pylint: disable=singleton-comparison
        if select is not None and np.any(np.array(select) == None):
            raise NotImplementedError("Post-selection lists must only contain numerical values.")

        # Make sure the state is mixed
        if self._pure:
            state = ops.mix(self._state, self._num_modes)
        else:
            state = self._state

        if select is not None:
            # perform post-selection

            # make sure modes and select are the same length
            if len(select) != len(modes):
                raise ValueError("When performing post-selection, the number of "
                                 "selected values (including None) must match the number of measured modes")

            # make sure the select values are all integers or nones
            if not all(isinstance(s, int) or s is None for s in select):
                raise TypeError("The post-select list elements either be integers or None")

            # modes to measure
            measure = [i for i, s in zip(modes, select) if s is None]

            # modes already post-selected:
            selected = [i for i, s in zip(modes, select) if s is not None]
            select_values = [s for s in select if s is not None]

            # project out postselected modes
            self._state = ops.project_reset(selected, select_values, self._state, self._pure, self._num_modes, self._trunc)

            if self.norm() == 0:
                raise ZeroDivisionError("Measurement has zero probability.")

            self._state = self._state / self.norm()

        else:
            # no post-selection; modes to measure are the modes provided
            measure = modes

        if len(measure) > 0:
            # sampling needs to be performed
            # Compute distribution by tracing out modes not measured, then computing the diagonal
            unmeasured = [i for i in range(self._num_modes) if i not in measure]
            reduced = ops.partial_trace(state, self._num_modes, unmeasured)
            dist = np.ravel(ops.diagonal(reduced, len(measure)).real)

            # Make a random choice
            if sum(dist) != 1:
                # WARNING: distribution is not normalized, could hide errors
                i = np.random.choice(list(range(len(dist))), p=dist / sum(dist))
            else:
                i = np.random.choice(list(range(len(dist))), p=dist)

            permuted_outcome = ops.unIndex(i, len(measure), self._trunc)

            # Permute the outcome to match the order of the modes in 'measure'
            permutation = np.argsort(measure)
            outcome = [0] * len(measure)
            for i in range(len(measure)):
                outcome[permutation[i]] = permuted_outcome[i]

            # Project the state onto the measurement outcome & reset in vacuum
            self._state = ops.project_reset(measure, outcome, self._state, self._pure, self._num_modes, self._trunc)

            if self.norm() == 0:
                raise ZeroDivisionError("Measurement has zero probability.")

            self._state = self._state / self.norm()

        # include post-selected values in measurement outcomes
        if select is not None:
            outcome = copy.copy(select)

        return outcome

    def measure_homodyne(self, phi, mode, select=None, **kwargs):
        """
        Performs a homodyne measurement on a mode.
        """
        m_omega_over_hbar = 1/self._hbar

        # Make sure the state is mixed for reduced density matrix
        if self._pure:
            state = ops.mix(self._state, self._num_modes)
        else:
            state = self._state

        if select is not None:
            meas_result = select
            if isinstance(meas_result, numbers.Number):
                homodyne_sample = float(meas_result)
            else:
                raise TypeError("Selected measurement result must be of numeric type.")
        else:
             # Compute reduced density matrix
            unmeasured = [i for i in range(self._num_modes) if not i == mode]
            reduced = ops.partial_trace(state, self._num_modes, unmeasured)

            # Rotate to measurement basis
            args = [ops.phase(-phi, self._trunc), reduced, False, [0], 1, self._trunc]
            if self._mode == 'blas':
                reduced = ops.apply_gate_BLAS(*args)
            elif self._mode == 'einsum':
                reduced = ops.apply_gate_einsum(*args)

            # Create pdf. Same as tf implementation, but using
            # the recursive relation H_0(x) = 1, H_1(x) = 2x, H_{n+1}(x) = 2xH_n(x) - 2nH_{n-1}(x)
            q_mag = kwargs.get('max', 10)
            num_bins = kwargs.get('num_bins', 100000)

            q_tensor, Hvals = ops.hermiteVals(q_mag, num_bins, m_omega_over_hbar, self._trunc)
            H_matrix = np.zeros((self._trunc, self._trunc, num_bins))
            for n, m in product(range(self._trunc), repeat=2):
                H_matrix[n][m] = 1 / sqrt(2**n * bang(n) * 2**m * bang(m)) * Hvals[n] * Hvals[m]
            H_terms = np.expand_dims(reduced, -1) * np.expand_dims(H_matrix, 0)
            rho_dist = np.sum(H_terms, axis=(1, 2)) \
                                 * (m_omega_over_hbar/pi)**0.5 \
                                 * np.exp(-m_omega_over_hbar * q_tensor**2) \
                                 * (q_tensor[1] - q_tensor[0]) # Delta_q for normalization (only works if the bins are equally spaced)

            # Sample from rho_dist. This is a bit different from tensorflow due to how
            # numpy treats multinomial sampling. In particular, numpy returns a
            # histogram of the samples whereas tensorflow gives the list of samples.
            # Numpy also does not use the log probabilities
            probs = rho_dist.flatten().real
            probs /= np.sum(probs)
            sample_hist = np.random.multinomial(1, probs)
            sample_idx = list(sample_hist).index(1)
            homodyne_sample = q_tensor[sample_idx]

        # Project remaining modes into the conditional state
        inf_squeezed_vac = \
            np.array([(-0.5)**(n//2) * sqrt(bang(n)) / bang(n//2) if n%2 == 0 else 0.0 + 0.0j \
                for n in range(self._trunc)], dtype=ops.def_type)
        alpha = homodyne_sample * sqrt(m_omega_over_hbar / 2)

        composed = np.dot(ops.phase(phi, self._trunc), ops.displacement(alpha, self._trunc))
        args = [composed, inf_squeezed_vac, True, [0], 1, self._trunc]
        if self._mode == 'blas':
            eigenstate = ops.apply_gate_BLAS(*args)
        elif self._mode == 'einsum':
            eigenstate = ops.apply_gate_einsum(*args)

        vac_state = np.array([1.0 + 0.0j if i == 0 else 0.0 + 0.0j for i in range(self._trunc)], dtype=ops.def_type)
        projector = np.outer(vac_state, eigenstate.conj())
        self._apply_gate(projector, [mode])

        # Normalize
        self._state = self._state / self.norm()

        return homodyne_sample

# Copyright 2019 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.

"""
Fock backend proper
======================

Contains most of the code for managing the simulator state and offloading
operations to the utilities in ops.

Hyperlinks: :class:`Circuit`

.. currentmodule:: strawberryfields.backends.fockbackend.circuit

Contents
----------------------
.. autosummary::
     Circuit

"""
# pylint: disable=too-many-arguments,len-as-condition,attribute-defined-outside-init
# pylint: disable=too-many-branches,too-many-locals,too-many-public-methods

import copy
import string
import numbers
from itertools import product

import numpy as np
from numpy import sqrt, pi
from scipy.special import factorial as bang

from . import ops

indices = string.ascii_lowercase
MAX_MODES = len(indices) - 3

class Circuit():
    """
    Class implementing a basic simulator for a collection of modes
    in the fock basis.
    """

    def __init__(self, num, trunc, hbar=2, pure=True, do_checks=False, mode='blas'):
        r"""Class initializer.

        Args:
            num (non-negative int): Number of modes in the register.
            trunc (positive int): Truncation parameter.  Fock states up to |trunc-1> are representable.
            hbar (int): The value of :math:`\hbar` to initialise the circuit with, depending on the conventions followed.
                By default, :math:`\hbar=2`. See :ref:`conventions` for more details.
            pure (bool, optional): Whether states are pure (True) or mixed (False)
            do_checks (bool, optional): Whether arguments are to be checked first
            mode (str, optional): Whether to use BLAS or einsum for matrix operations.
        """

        # Check validity
        if num < 0:
            raise ValueError("Number of modes must be non-negative -- got {}".format(num))
        if num > MAX_MODES:
            raise ValueError("Fock simulator has a maximum of {} modes".format(MAX_MODES))
        if trunc <= 0:
            raise ValueError("Truncation must be positive -- got {}".format(trunc))

        self._num_modes = num
        self._hbar = hbar
        self._checks = do_checks
        self._mode = mode
        self.reset(pure=pure, cutoff_dim=trunc)

    def _apply_gate(self, mat, modes):
        """Master gate application function. Selects between implementations based
        on the `_mode` class parameter.

        Args:
            mat (array): The matrix to apply
            modes (list<non-negative int>): The modes to apply `mat` to
        """

        args = [mat, self._state, self._pure, modes, self._num_modes, self._trunc]
        if self._mode == 'blas':
            self._state = ops.apply_gate_BLAS(*args)
        elif self._mode == 'einsum':
            self._state = ops.apply_gate_einsum(*args)
        else:
            raise NotImplementedError

    def _apply_channel(self, kraus_ops, modes):
        """Master channel application function. Applies a channel represented by
        Kraus operators.

        .. note::
                Always results in a mixed state.

        Args:
            kraus_ops (list<array>): A list of Kraus operators
            modes (list<non-negative int>): The modes to apply the channel to
        """

        if self._pure:
            self._state = ops.mix(self._state, self._num_modes)
            self._pure = False

        if len(kraus_ops) == 0:
            self._state = np.zeros([self._trunc for i in range(self._num_modes*2)], dtype=ops.def_type)
        elif self._mode == 'blas':
            states = [ops.apply_gate_einsum(k, np.copy(self._state), False, modes, self._num_modes, self._trunc)\
                                    for k in kraus_ops]
            self._state = sum(states)
        elif self._mode == 'einsum':
            states = [ops.apply_gate_einsum(k, self._state, False, modes, self._num_modes, self._trunc)\
                                    for k in kraus_ops]
            self._state = sum(states)


    def reset(self, pure=None, cutoff_dim=None, num_subsystems=None):
        """Resets the simulation state.

        Args:
            pure (bool, optional): Sets the purity setting. Default is unchanged.
            cutoff_dim (int): New Hilbert space truncation dimension.
            num_subsystems (int, optional): Sets the number of modes in the reset
                circuit. Default is unchanged.
        """
        if pure is not None:
            if not isinstance(pure, bool):
                raise ValueError("Argument 'pure' must be either True or False")
            self._pure = pure

        if num_subsystems is not None:
            if not isinstance(num_subsystems, int):
                raise ValueError("Argument 'num_subsystems' must be a positive integer")
            self._num_modes = num_subsystems

        if cutoff_dim is not None:
            if not isinstance(cutoff_dim, int) or cutoff_dim < 1:
                raise ValueError("Argument 'cutoff_dim' must be a positive integer")
            self._trunc = cutoff_dim

        if self._pure:
            self._state = ops.vacuumState(self._num_modes, self._trunc)
        else:
            self._state = ops.vacuumStateMixed(self._num_modes, self._trunc)

    def norm(self):
        """returns the norm of the state"""
        if self._pure:
            return sqrt(np.vdot(self._state, self._state).real)
        return ops.trace(self._state, self._num_modes)

    def alloc(self, n=1):
        """allocate a number of modes at the end of the state."""
        # base_shape = [self._trunc for i in range(n)]
        if self._pure:
            vac = ops.vacuumState(n, self._trunc)
        else:
            vac = ops.vacuumStateMixed(n, self._trunc)

        self._state = ops.tensor(self._state, vac, self._num_modes, self._pure)
        self._num_modes = self._num_modes + n

    def dealloc(self, modes):
        """Traces out and deallocates the modes in `modes`"""
        if self._pure:
            self._state = ops.mix(self._state, self._num_modes)
            self._pure = False

        self._state = ops.partial_trace(self._state, self._num_modes, modes)
        self._num_modes = self._num_modes - len(modes)

    def prepare_multimode(self, state, modes):
        r"""
        Prepares a given mode or list of modes in the given state.

        After the preparation the system is in a mixed product state,
        with the specified modes replaced by state.
        The given state can be either in tensor form or in matrix/vector form.
        If modes is not ordered, the subsystems of the input are
        reordered to reflect that, i.e., if modes=[3,1], then the first mode
        of state ends up in mode 3 and the second mode of state ends up in
        mode 1 of the output state.
        If modes is None, it is attempted to prepare state in all modes.
        The reduced state on all other modes remains unchanged and
        the final state is product with respect to the partition into
        the modes in modes and the complement.

        Args:
            state (array): vector, matrix, or tensor representation of the ket state or dm state in the fock basis to prepare
            modes (list[int] or non-negative int): The mode(s) into which state is to be prepared. Needs not be ordered.
        """
        if isinstance(modes, int):
            modes = [modes]

        n_modes = len(modes)
        pure_shape = tuple([self._trunc]*n_modes)
        mixed_shape = tuple([self._trunc]*(2*n_modes))
        pure_shape_as_vector = tuple([self._trunc**n_modes])
        mixed_shape_as_matrix = tuple([self._trunc**n_modes]*2)

        # Do consistency checks
        if self._checks:
            if state.shape != pure_shape and state.shape != mixed_shape \
               and \
               state.shape != pure_shape_as_vector and state.shape != mixed_shape_as_matrix:
                raise ValueError("Incorrect shape for state preparation")
            if len(modes) != len(set(modes)):
                raise ValueError("The specified modes cannot appear multiple times.")

        # reshape to support input both as tensor and vector/matrix
        if state.shape == pure_shape_as_vector:
            state = state.reshape(pure_shape)
        elif state.shape == mixed_shape_as_matrix:
            state = state.reshape(mixed_shape)

        if self._num_modes == n_modes:
            # Hack for marginally faster state preparation
            self._state = state.astype(ops.def_type)
            self._pure = bool(state.shape == pure_shape)
        else:
            if self._pure:
                self._state = ops.mix(self._state, self._num_modes)
                self._pure = False

            if state.shape == pure_shape:
                state = ops.mix(state, len(modes))

            # Take the partial trace
            # todo: For performance the partial trace could be done directly from the pure state. This would of course require a better partial trace function...
            reduced_state = ops.partial_trace(self._state, self._num_modes, modes)

            # Insert state at the end (I know there is also tensor() from ops but it has extra aguments wich only confuse here)
            self._state = np.tensordot(reduced_state, state, axes=0)

            # unless the preparation was meant to go into the last modes in the standard order, we need to swap indices around
        if modes != list(range(self._num_modes-len(modes), self._num_modes)):
            mode_permutation = [x for x in range(self._num_modes) if x not in modes] + modes
            if self._pure:
                scale = 1
                index_permutation = mode_permutation
            else:
                scale = 2
                index_permutation = [scale*x+i for x in mode_permutation for i in (0, 1)] #two indices per mode if we have pure states
            index_permutation = np.argsort(index_permutation)

            self._state = np.transpose(self._state, index_permutation)

    def prepare(self, state, mode):
        r"""
        Prepares a given mode in a given state.

        This is a simple wrapper for prepare_multimode(), see there for more details.

        Args:
            state (array): vector, matrix, or tensor representation of the ket state or dm state in the fock basis to prepare
            modes (list[int] or non-negative int or None): The mode(s) into which state is to be prepared. Needs not be ordered.
        """
        if isinstance(mode, int):
            mode = [mode]
        self.prepare_multimode(state, mode)

    def prepare_mode_fock(self, n, mode):
        """
        Prepares a mode in a fock state.
        """

        if self._pure:
            self.prepare(ops.fockState(n, self._trunc), mode)
        else:
            st = ops.fockState(n, self._trunc)
            self.prepare(np.outer(st, st.conjugate()), mode)

    def prepare_mode_coherent(self, alpha, mode):
        """
        Prepares a mode in a coherent state.
        """
        if self._pure:
            self.prepare(ops.coherentState(alpha, self._trunc), mode)
        else:
            st = ops.coherentState(alpha, self._trunc)
            self.prepare(np.outer(st, st.conjugate()), mode)

    def prepare_mode_squeezed(self, r, theta, mode):
        """
        Prepares a mode in a squeezed state.
        """
        if self._pure:
            self.prepare(ops.squeezedState(r, theta, self._trunc), mode)
        else:
            st = ops.squeezedState(r, theta, self._trunc)
            self.prepare(np.outer(st, st.conjugate()), mode)

    def prepare_mode_displaced_squeezed(self, alpha, r, phi, mode):
        """
        Prepares a mode in a displaced squeezed state.
        """
        if self._pure:
            self.prepare(ops.displacedSqueezed(alpha, r, phi, self._trunc), mode)
        else:
            st = ops.displacedSqueezed(alpha, r, phi, self._trunc)
            self.prepare(np.outer(st, st.conjugate()), mode)

    def prepare_mode_thermal(self, nbar, mode):
        """
        Prepares a mode in a thermal state.
        """
        st = ops.thermalState(nbar, self._trunc)
        self.prepare(st, mode)

    def phase_shift(self, theta, mode):
        """
        Applies a phase shifter.
        """
        self._apply_gate(ops.phase(theta, self._trunc), [mode])

    def displacement(self, alpha, mode):
        """
        Applies a displacement gate.
        """
        self._apply_gate(ops.displacement(alpha, self._trunc), [mode])

    def beamsplitter(self, t, r, phi, mode1, mode2):
        """
        Applies a beamsplitter.
        """
        self._apply_gate(ops.beamsplitter(t, r, phi, self._trunc), [mode1, mode2])

    def squeeze(self, r, theta, mode):
        """
        Applies a squeezing gate.
        """
        self._apply_gate(ops.squeezing(r, theta, self._trunc), [mode])

    def kerr_interaction(self, kappa, mode):
        """
        Applies a Kerr interaction gate.
        """
        self._apply_gate(ops.kerr(kappa, self._trunc), [mode])

    def cross_kerr_interaction(self, kappa, mode1, mode2):
        """
        Applies a cross-Kerr interaction gate.
        """
        self._apply_gate(ops.cross_kerr(kappa, self._trunc), [mode1, mode2])

    def cubic_phase_shift(self, gamma, mode):
        """
        Applies a cubic phase shift gate.
        """
        self._apply_gate(ops.cubicPhase(gamma, self._hbar, self._trunc), [mode])

    def is_vacuum(self, tol):
        """
        Tests whether the system is in the vacuum state.
        """
        # base_shape = [self._trunc for i in range(self._num_modes)]
        if self._pure:
            vac = ops.vacuumState(self._num_modes, self._trunc)
        else:
            vac = ops.vacuumStateMixed(self._num_modes, self._trunc)
        return np.linalg.norm((self._state - vac).ravel()) < tol

    def get_state(self):
        """
        Returns the state of the system in the fock basis along with its purity.
        """
        return self._state, self._pure

    def loss(self, T, mode):
        """
        Applies a loss channel to the state.
        """
        self._apply_channel(ops.lossChannel(T, self._trunc), [mode])

    def measure_fock(self, modes, select=None):
        """
        Measures a list of modes.
        """
        # pylint: disable=singleton-comparison
        if select is not None and np.any(np.array(select) == None):
            raise NotImplementedError("Post-selection lists must only contain numerical values.")

        # Make sure the state is mixed
        if self._pure:
            state = ops.mix(self._state, self._num_modes)
        else:
            state = self._state

        if select is not None:
            # perform post-selection

            # make sure modes and select are the same length
            if len(select) != len(modes):
                raise ValueError("When performing post-selection, the number of "
                                 "selected values (including None) must match the number of measured modes")

            # make sure the select values are all integers or nones
            if not all(isinstance(s, int) or s is None for s in select):
                raise TypeError("The post-select list elements either be integers or None")

            # modes to measure
            measure = [i for i, s in zip(modes, select) if s is None]

            # modes already post-selected:
            selected = [i for i, s in zip(modes, select) if s is not None]
            select_values = [s for s in select if s is not None]

            # project out postselected modes
            self._state = ops.project_reset(selected, select_values, self._state, self._pure, self._num_modes, self._trunc)

            if self.norm() == 0:
                raise ZeroDivisionError("Measurement has zero probability.")

            self._state = self._state / self.norm()

        else:
            # no post-selection; modes to measure are the modes provided
            measure = modes

        if len(measure) > 0:
            # sampling needs to be performed
            # Compute distribution by tracing out modes not measured, then computing the diagonal
            unmeasured = [i for i in range(self._num_modes) if i not in measure]
            reduced = ops.partial_trace(state, self._num_modes, unmeasured)
            dist = np.ravel(ops.diagonal(reduced, len(measure)).real)

            # Make a random choice
            if sum(dist) != 1:
                # WARNING: distribution is not normalized, could hide errors
                i = np.random.choice(list(range(len(dist))), p=dist / sum(dist))
            else:
                i = np.random.choice(list(range(len(dist))), p=dist)

            permuted_outcome = ops.unIndex(i, len(measure), self._trunc)

            # Permute the outcome to match the order of the modes in 'measure'
            permutation = np.argsort(measure)
            outcome = [0] * len(measure)
            for i in range(len(measure)):
                outcome[permutation[i]] = permuted_outcome[i]

            # Project the state onto the measurement outcome & reset in vacuum
            self._state = ops.project_reset(measure, outcome, self._state, self._pure, self._num_modes, self._trunc)

            if self.norm() == 0:
                raise ZeroDivisionError("Measurement has zero probability.")

            self._state = self._state / self.norm()

        # include post-selected values in measurement outcomes
        if select is not None:
            outcome = copy.copy(select)

        return outcome

    def measure_homodyne(self, phi, mode, select=None, **kwargs):
        """
        Performs a homodyne measurement on a mode.
        """
        m_omega_over_hbar = 1/self._hbar

        # Make sure the state is mixed for reduced density matrix
        if self._pure:
            state = ops.mix(self._state, self._num_modes)
        else:
            state = self._state

        if select is not None:
            meas_result = select
            if isinstance(meas_result, numbers.Number):
                homodyne_sample = float(meas_result)
            else:
                raise TypeError("Selected measurement result must be of numeric type.")
        else:
             # Compute reduced density matrix
            unmeasured = [i for i in range(self._num_modes) if not i == mode]
            reduced = ops.partial_trace(state, self._num_modes, unmeasured)

            # Rotate to measurement basis
            args = [ops.phase(-phi, self._trunc), reduced, False, [0], 1, self._trunc]
            if self._mode == 'blas':
                reduced = ops.apply_gate_BLAS(*args)
            elif self._mode == 'einsum':
                reduced = ops.apply_gate_einsum(*args)

            # Create pdf. Same as tf implementation, but using
            # the recursive relation H_0(x) = 1, H_1(x) = 2x, H_{n+1}(x) = 2xH_n(x) - 2nH_{n-1}(x)
            q_mag = kwargs.get('max', 10)
            num_bins = kwargs.get('num_bins', 100000)

            q_tensor, Hvals = ops.hermiteVals(q_mag, num_bins, m_omega_over_hbar, self._trunc)
            H_matrix = np.zeros((self._trunc, self._trunc, num_bins))
            for n, m in product(range(self._trunc), repeat=2):
                H_matrix[n][m] = 1 / sqrt(2**n * bang(n) * 2**m * bang(m)) * Hvals[n] * Hvals[m]
            H_terms = np.expand_dims(reduced, -1) * np.expand_dims(H_matrix, 0)
            rho_dist = np.sum(H_terms, axis=(1, 2)) \
                                 * (m_omega_over_hbar/pi)**0.5 \
                                 * np.exp(-m_omega_over_hbar * q_tensor**2) \
                                 * (q_tensor[1] - q_tensor[0]) # Delta_q for normalization (only works if the bins are equally spaced)

            # Sample from rho_dist. This is a bit different from tensorflow due to how
            # numpy treats multinomial sampling. In particular, numpy returns a
            # histogram of the samples whereas tensorflow gives the list of samples.
            # Numpy also does not use the log probabilities
            probs = rho_dist.flatten().real
            probs /= np.sum(probs)
            sample_hist = np.random.multinomial(1, probs)
            sample_idx = list(sample_hist).index(1)
            homodyne_sample = q_tensor[sample_idx]

        # Project remaining modes into the conditional state
        inf_squeezed_vac = \
            np.array([(-0.5)**(n//2) * sqrt(bang(n)) / bang(n//2) if n%2 == 0 else 0.0 + 0.0j \
                for n in range(self._trunc)], dtype=ops.def_type)
        alpha = homodyne_sample * sqrt(m_omega_over_hbar / 2)

        composed = np.dot(ops.phase(phi, self._trunc), ops.displacement(alpha, self._trunc))
        args = [composed, inf_squeezed_vac, True, [0], 1, self._trunc]
        if self._mode == 'blas':
            eigenstate = ops.apply_gate_BLAS(*args)
        elif self._mode == 'einsum':
            eigenstate = ops.apply_gate_einsum(*args)

        vac_state = np.array([1.0 + 0.0j if i == 0 else 0.0 + 0.0j for i in range(self._trunc)], dtype=ops.def_type)
        projector = np.outer(vac_state, eigenstate.conj())
        self._apply_gate(projector, [mode])

        # Normalize
        self._state = self._state / self.norm()

        return homodyne_sample
# Copyright 2019 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. Fock backend proper ======================  Contains most of the code for managing the simulator state and offloading operations to the utilities in ops.  Hyperlinks: :class:`Circuit`  .. currentmodule:: strawberryfields.backends.fockbackend.circuit  Contents ---------------------- .. autosummary::      Circuit # pylint: disable=too-many-arguments,len-as-condition,attribute-defined-outside-init # pylint: disable=too-many-branches,too-many-locals,too-many-public-methods Class implementing a basic simulator for a collection of modes     in the fock basis. Class initializer.          Args:             num (non-negative int): Number of modes in the register.             trunc (positive int): Truncation parameter.  Fock states up to |trunc-1> are representable.             hbar (int): The value of :math:`\hbar` to initialise the circuit with, depending on the conventions followed.                 By default, :math:`\hbar=2`. See :ref:`conventions` for more details.             pure (bool, optional): Whether states are pure (True) or mixed (False)             do_checks (bool, optional): Whether arguments are to be checked first             mode (str, optional): Whether to use BLAS or einsum for matrix operations. # Check validity Master gate application function. Selects between implementations based         on the `_mode` class parameter.          Args:             mat (array): The matrix to apply             modes (list<non-negative int>): The modes to apply `mat` to Master channel application function. Applies a channel represented by         Kraus operators.          .. note::                 Always results in a mixed state.          Args:             kraus_ops (list<array>): A list of Kraus operators             modes (list<non-negative int>): The modes to apply the channel to Resets the simulation state.          Args:             pure (bool, optional): Sets the purity setting. Default is unchanged.             cutoff_dim (int): New Hilbert space truncation dimension.             num_subsystems (int, optional): Sets the number of modes in the reset                 circuit. Default is unchanged. returns the norm of the state allocate a number of modes at the end of the state. # base_shape = [self._trunc for i in range(n)] Traces out and deallocates the modes in `modes` Prepares a given mode or list of modes in the given state.          After the preparation the system is in a mixed product state,         with the specified modes replaced by state.         The given state can be either in tensor form or in matrix/vector form.         If modes is not ordered, the subsystems of the input are         reordered to reflect that, i.e., if modes=[3,1], then the first mode         of state ends up in mode 3 and the second mode of state ends up in         mode 1 of the output state.         If modes is None, it is attempted to prepare state in all modes.         The reduced state on all other modes remains unchanged and         the final state is product with respect to the partition into         the modes in modes and the complement.          Args:             state (array): vector, matrix, or tensor representation of the ket state or dm state in the fock basis to prepare             modes (list[int] or non-negative int): The mode(s) into which state is to be prepared. Needs not be ordered. # Do consistency checks # reshape to support input both as tensor and vector/matrix # Hack for marginally faster state preparation # Take the partial trace # todo: For performance the partial trace could be done directly from the pure state. This would of course require a better partial trace function... # Insert state at the end (I know there is also tensor() from ops but it has extra aguments wich only confuse here) # unless the preparation was meant to go into the last modes in the standard order, we need to swap indices around #two indices per mode if we have pure states Prepares a given mode in a given state.          This is a simple wrapper for prepare_multimode(), see there for more details.          Args:             state (array): vector, matrix, or tensor representation of the ket state or dm state in the fock basis to prepare             modes (list[int] or non-negative int or None): The mode(s) into which state is to be prepared. Needs not be ordered. Prepares a mode in a fock state. Prepares a mode in a coherent state. Prepares a mode in a squeezed state. Prepares a mode in a displaced squeezed state. Prepares a mode in a thermal state. Applies a phase shifter. Applies a displacement gate. Applies a beamsplitter. Applies a squeezing gate. Applies a Kerr interaction gate. Applies a cross-Kerr interaction gate. Applies a cubic phase shift gate. Tests whether the system is in the vacuum state. # base_shape = [self._trunc for i in range(self._num_modes)] Returns the state of the system in the fock basis along with its purity. Applies a loss channel to the state. Measures a list of modes. # pylint: disable=singleton-comparison # Make sure the state is mixed # perform post-selection # make sure modes and select are the same length # make sure the select values are all integers or nones # modes to measure # modes already post-selected: # project out postselected modes # no post-selection; modes to measure are the modes provided # sampling needs to be performed # Compute distribution by tracing out modes not measured, then computing the diagonal # Make a random choice # WARNING: distribution is not normalized, could hide errors # Permute the outcome to match the order of the modes in 'measure' # Project the state onto the measurement outcome & reset in vacuum # include post-selected values in measurement outcomes Performs a homodyne measurement on a mode. # Make sure the state is mixed for reduced density matrix # Compute reduced density matrix # Rotate to measurement basis # Create pdf. Same as tf implementation, but using # the recursive relation H_0(x) = 1, H_1(x) = 2x, H_{n+1}(x) = 2xH_n(x) - 2nH_{n-1}(x) # Delta_q for normalization (only works if the bins are equally spaced) # Sample from rho_dist. This is a bit different from tensorflow due to how # numpy treats multinomial sampling. In particular, numpy returns a # histogram of the samples whereas tensorflow gives the list of samples. # Numpy also does not use the log probabilities # Project remaining modes into the conditional state # Normalize