Source code for strawberryfields.backends.tfbackend.backend
# 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.
"""
TensorFlow backend interface
============================
"""
# pylint: disable=too-many-public-methods
import numpy as np
import tensorflow as tf
from strawberryfields.backends import BaseFock, ModeMap
from .circuit import Circuit
from .ops import mix, partial_trace, reorder_modes
from .states import FockStateTF
[docs]class TFBackend(BaseFock):
r"""Implements a simulation of quantum optical circuits in a truncated
Fock basis using `TensorFlow <http://tensorflow.org/>`_, returning a :class:`~.FockStateTF`
state object.
"""
short_name = "tf"
def __init__(self):
"""Initialize a TFBackend object."""
super().__init__()
self._supported["mixed_states"] = True
self._supported["batched"] = True
self._supported["symbolic"] = True
self._init_modes = None #: int: initial number of modes in the circuit
self._modemap = None #: Modemap: maps external mode indices to internal ones
self.circuit = (
None #: ~.tfbackend.circuit.Circuit: representation of the simulated quantum state
)
def _remap_modes(self, modes):
if isinstance(modes, int):
modes = [modes]
was_int = True
else:
was_int = False
map_ = self._modemap.show()
submap = [map_[m] for m in modes]
if not self._modemap.valid(modes) or None in submap:
raise ValueError("The specified modes are not valid.")
remapped_modes = self._modemap.remap(modes)
if was_int:
remapped_modes = remapped_modes[0]
return remapped_modes
[docs] def begin_circuit(self, num_subsystems, **kwargs):
r"""Instantiate a quantum circuit.
Instantiates a representation of a quantum optical state with ``num_subsystems`` modes.
The state is initialized to vacuum.
The modes in the circuit are indexed sequentially using integers, starting from zero.
Once an index is assigned to a mode, it can never be re-assigned to another mode.
If the mode is deleted its index becomes invalid.
An operation acting on an invalid or unassigned mode index raises an ``IndexError`` exception.
Args:
num_subsystems (int): number of modes in the circuit
Keyword Args:
cutoff_dim (int): Numerical Hilbert space cutoff dimension for the modes.
For each mode, the simulator can represent the Fock states :math:`\ket{0}, \ket{1}, \ldots, \ket{\text{cutoff_dim}-1}`.
pure (bool): If True (default), use a pure state representation (otherwise will use a mixed state representation).
batch_size (None or int): Size of the batch-axis dimension. If None, no batch-axis will be used.
dtype (tf.DType): Complex Tensorflow Tensor type representation, either ``tf.complex64`` (default) or ``tf.complex128``.
Note, ``tf.complex128`` will increase memory usage substantially.
"""
cutoff_dim = kwargs.get("cutoff_dim", None)
pure = kwargs.get("pure", True)
batch_size = kwargs.get("batch_size", None)
dtype = kwargs.get("dtype", tf.complex64)
if cutoff_dim is None:
raise ValueError("Argument 'cutoff_dim' must be passed to the TensorFlow backend")
if not isinstance(num_subsystems, int):
raise ValueError("Argument 'num_subsystems' must be a positive integer")
if not isinstance(cutoff_dim, int):
raise ValueError("Argument 'cutoff_dim' must be a positive integer")
if not isinstance(pure, bool):
raise ValueError("Argument 'pure' must be either True or False")
if not dtype in (tf.complex64, tf.complex128):
raise ValueError("Argument 'dtype' must be a complex Tensorflow DType")
if batch_size == 1:
raise ValueError(
"batch_size of 1 not supported, please use different batch_size or set batch_size=None"
)
with tf.name_scope("Begin_circuit"):
self._modemap = ModeMap(num_subsystems)
circuit = Circuit(num_subsystems, cutoff_dim, pure, batch_size, dtype)
self._init_modes = num_subsystems
self.circuit = circuit
[docs] def reset(self, pure=True, **kwargs):
"""Reset the circuit so that all the modes are in the vacuum state.
After the reset the circuit is in the same state as it was after
the last :meth:`begin_circuit` call. It will have the original number
of modes, all initialized in the vacuum state. Some circuit parameters
may be changed during the reset, see the keyword args below.
Args:
pure (bool): if True, initialize the circuit in a pure state representation
(will use a mixed state representation if pure is False)
Keyword Args:
cutoff_dim (int): new Hilbert space truncation dimension
"""
with tf.name_scope("Reset"):
self._modemap.reset()
self.circuit.reset(num_subsystems=self._init_modes, pure=pure, **kwargs)
[docs] def get_modes(self):
# pylint: disable=protected-access
return [i for i, j in enumerate(self._modemap._map) if j is not None]
[docs] def prepare_vacuum_state(self, mode):
with tf.name_scope("Prepare_vacuum"):
remapped_mode = self._remap_modes(mode)
self.circuit.prepare_vacuum_state(remapped_mode)
[docs] def prepare_coherent_state(self, r, phi, mode):
with tf.name_scope("Prepare_coherent"):
remapped_mode = self._remap_modes(mode)
self.circuit.prepare_coherent_state(r, phi, remapped_mode)
[docs] def prepare_squeezed_state(self, r, phi, mode):
with tf.name_scope("Prepare_squeezed"):
remapped_mode = self._remap_modes(mode)
self.circuit.prepare_squeezed_state(r, phi, remapped_mode)
[docs] def prepare_displaced_squeezed_state(self, r_d, phi_d, r_s, phi_s, mode):
with tf.name_scope("Prepare_displaced_squeezed"):
remapped_mode = self._remap_modes(mode)
self.circuit.prepare_displaced_squeezed_state(r_d, phi_d, r_s, phi_s, remapped_mode)
[docs] def prepare_fock_state(self, n, mode):
with tf.name_scope("Prepare_fock"):
remapped_mode = self._remap_modes(mode)
self.circuit.prepare_fock_state(n, remapped_mode)
[docs] def prepare_ket_state(self, state, modes):
with tf.name_scope("Prepare_state"):
self.circuit.prepare_multimode(state, self._remap_modes(modes), True)
[docs] def prepare_dm_state(self, state, modes):
with tf.name_scope("Prepare_state"):
self.circuit.prepare_multimode(state, self._remap_modes(modes), False)
[docs] def prepare_thermal_state(self, nbar, mode):
with tf.name_scope("Prepare_thermal"):
remapped_mode = self._remap_modes(mode)
self.circuit.prepare_thermal_state(nbar, remapped_mode)
[docs] def rotation(self, phi, mode):
with tf.name_scope("Rotation"):
remapped_mode = self._remap_modes(mode)
self.circuit.phase_shift(phi, remapped_mode)
[docs] def displacement(self, r, phi, mode):
with tf.name_scope("Displacement"):
remapped_mode = self._remap_modes(mode)
self.circuit.displacement(r, phi, remapped_mode)
[docs] def squeeze(self, r, phi, mode):
with tf.name_scope("Squeeze"):
remapped_mode = self._remap_modes(mode)
self.circuit.squeeze(r, phi, remapped_mode)
[docs] def beamsplitter(self, theta, phi, mode1, mode2):
with tf.name_scope("Beamsplitter"):
remapped_modes = self._remap_modes([mode1, mode2])
self.circuit.beamsplitter(theta, phi, remapped_modes[0], remapped_modes[1])
[docs] def mzgate(self, phi_in, phi_ex, mode1, mode2):
with tf.name_scope("MZgate"):
remapped_modes = self._remap_modes([mode1, mode2])
self.circuit.mzgate(phi_in, phi_ex, remapped_modes[0], remapped_modes[1])
[docs] def two_mode_squeeze(self, r, phi, mode1, mode2):
with tf.name_scope("Two-mode_squeezing"):
remapped_modes = self._remap_modes([mode1, mode2])
self.circuit.two_mode_squeeze(r, phi, remapped_modes[0], remapped_modes[1])
[docs] def loss(self, T, mode):
with tf.name_scope("Loss"):
remapped_mode = self._remap_modes(mode)
self.circuit.loss(T, remapped_mode)
[docs] def cubic_phase(self, gamma, mode):
with tf.name_scope("Cubic_phase"):
remapped_mode = self._remap_modes(mode)
self.circuit.cubic_phase(gamma, remapped_mode)
[docs] def kerr_interaction(self, kappa, mode):
with tf.name_scope("Kerr_interaction"):
remapped_mode = self._remap_modes(mode)
self.circuit.kerr_interaction(kappa, remapped_mode)
[docs] def cross_kerr_interaction(self, kappa, mode1, mode2):
with tf.name_scope("Cross-Kerr_interaction"):
remapped_modes = self._remap_modes([mode1, mode2])
self.circuit.cross_kerr_interaction(kappa, remapped_modes[0], remapped_modes[1])
[docs] def gaussian_gate(self, S, d, *modes):
"""Multimode gaussian gate parametrized by its symplectic matrix and displacement vector, the number of modes to be applied is not fixed"""
with tf.name_scope("Gaussian_gate"):
remapped_modes = self._remap_modes(modes)
self.circuit.gaussian_gate(S, d, remapped_modes)
[docs] def state(self, modes=None, **kwargs):
r"""Returns the state of the quantum simulation, restricted to the subsystems defined by `modes`.
See :meth:`.BaseBackend.state`.
Returns:
FockStateTF: state description
"""
with tf.name_scope("State"):
s = self.circuit.state
pure = self.circuit.state_is_pure
num_modes = self.circuit.num_modes
batched = self.circuit.batched
dtype = self.circuit.dtype
# reduce rho down to specified subsystems
if modes is None:
# reduced state is full state
reduced_state = s
modes = list(range(num_modes))
else:
if isinstance(modes, int):
modes = [modes]
if len(modes) != len(set(modes)):
raise ValueError("The specified modes cannot be duplicated.")
if len(modes) > num_modes:
raise ValueError(
"The number of specified modes cannot be larger than the number of subsystems."
)
if pure:
# convert to mixed state representation
reduced_state = mix(s, batched)
pure = False
else:
reduced_state = s
# trace our all modes not in modes
# todo: Doing this one by one is very inefficient. The partial trace function should be improved.
for mode in sorted([m for m in range(num_modes) if m not in modes], reverse=True):
reduced_state = partial_trace(reduced_state, mode, False, batched)
reduced_state_pure = False
# unless the modes were requested in order, we need to swap indices around
if modes != sorted(modes):
mode_permutation = np.argsort(np.argsort(modes))
reduced_state = reorder_modes(
reduced_state, mode_permutation, reduced_state_pure, batched
)
s = reduced_state
modenames = ["q[{}]".format(i) for i in np.array(self.get_modes())[modes]]
state_ = FockStateTF(
s,
len(modes),
pure,
self.circuit.cutoff_dim,
batched=batched,
mode_names=modenames,
dtype=dtype,
)
return state_
[docs] def measure_fock(self, modes, shots=1, select=None, **kwargs):
"""Measure the given modes in the Fock basis.
See :meth:`.BaseFock.measure_fock`.
Keyword Args:
Returns:
tuple[int] or tuple[Tensor]: measurement outcomes
"""
if shots != 1:
raise NotImplementedError(
"TF backend currently does not support " "shots != 1 for Fock measurement"
)
with tf.name_scope("Measure_fock"):
remapped_modes = self._remap_modes(modes)
meas = self.circuit.measure_fock(remapped_modes, select=select, **kwargs)
return meas
[docs] def measure_homodyne(self, phi, mode, shots=1, select=None, **kwargs):
"""Perform a homodyne measurement on the specified modes.
See :meth:`.BaseBackend.measure_homodyne`.
Keyword Args:
num_bins (int): Number of equally spaced bins for the probability distribution function
(pdf) simulating the homodyne measurement (default: 100000).
max (float): The pdf is discretized onto the 1D grid [-max,max] (default: 10).
Returns:
float or tf.Tensor: measurement outcome
"""
if shots != 1:
raise NotImplementedError(
"TF backend currently does not support " "shots != 1 for homodyne measurement"
)
with tf.name_scope("Measure_homodyne"):
remapped_mode = self._remap_modes(mode)
meas = self.circuit.measure_homodyne(phi, remapped_mode, select, **kwargs)
return meas
[docs] def is_vacuum(self, tol=0.0, **kwargs):
with tf.name_scope("Is_vacuum"):
vac_elem = self.circuit.vacuum_element()
return np.abs(vac_elem - 1) <= tol
[docs] def del_mode(self, modes):
with tf.name_scope("Del_mode"):
remapped_modes = self._remap_modes(modes)
if isinstance(remapped_modes, int):
remapped_modes = [remapped_modes]
self.circuit.del_mode(remapped_modes)
self._modemap.delete(modes)
[docs] def add_mode(self, n=1, **kwargs):
with tf.name_scope("Add_mode"):
self.circuit.add_mode(n)
self._modemap.add(n)
[docs] def measure_threshold(self, modes, shots=1, select=None, **kwargs):
raise NotImplementedError
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