Source code for pennylane.ops.qubit.state_preparation
# Copyright 2018-2021 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.
"""
This submodule contains the discrete-variable quantum operations concerned
with preparing a certain state on the device.
"""
# pylint:disable=abstract-method,arguments-differ,protected-access,no-member
import numpy as np
from pennylane import math
from pennylane.operation import AnyWires, Operation, StatePrepBase
from pennylane.templates.state_preparations import BasisStatePreparation, MottonenStatePreparation
from pennylane.wires import Wires, WireError
state_prep_ops = {"BasisState", "StatePrep", "QubitDensityMatrix"}
[docs]class BasisState(StatePrepBase):
r"""BasisState(n, wires)
Prepares a single computational basis state.
**Details:**
* Number of wires: Any (the operation can act on any number of wires)
* Number of parameters: 1
* Gradient recipe: None (integer parameters not supported)
.. note::
If the ``BasisState`` operation is not supported natively on the
target device, PennyLane will attempt to decompose the operation
into :class:`~.PauliX` operations.
.. note::
When called in the middle of a circuit, the action of the operation is defined
as :math:`U|0\rangle = |\psi\rangle`
Args:
n (array): prepares the basis state :math:`\ket{n}`, where ``n`` is an
array of integers from the set :math:`\{0, 1\}`, i.e.,
if ``n = np.array([0, 1, 0])``, prepares the state :math:`|010\rangle`.
wires (Sequence[int] or int): the wire(s) the operation acts on
id (str): custom label given to an operator instance,
can be useful for some applications where the instance has to be identified.
**Example**
>>> dev = qml.device('default.qubit', wires=2)
>>> @qml.qnode(dev)
... def example_circuit():
... qml.BasisState(np.array([1, 1]), wires=range(2))
... return qml.state()
>>> print(example_circuit())
[0.+0.j 0.+0.j 0.+0.j 1.+0.j]
"""
num_wires = AnyWires
num_params = 1
"""int: Number of trainable parameters that the operator depends on."""
ndim_params = (1,)
"""int: Number of dimensions per trainable parameter of the operator."""
[docs] @staticmethod
def compute_decomposition(n, wires):
r"""Representation of the operator as a product of other operators (static method). :
.. math:: O = O_1 O_2 \dots O_n.
.. seealso:: :meth:`~.BasisState.decomposition`.
Args:
n (array): prepares the basis state :math:`\ket{n}`, where ``n`` is an
array of integers from the set :math:`\{0, 1\}`
wires (Iterable, Wires): the wire(s) the operation acts on
Returns:
list[Operator]: decomposition into lower level operations
**Example:**
>>> qml.BasisState.compute_decomposition([1,0], wires=(0,1))
[BasisStatePreparation([1, 0], wires=[0, 1])]
"""
return [BasisStatePreparation(n, wires)]
[docs] def state_vector(self, wire_order=None):
"""Returns a state-vector of shape ``(2,) * num_wires``."""
prep_vals = self.parameters[0]
if any(i not in [0, 1] for i in prep_vals):
raise ValueError("BasisState parameter must consist of 0 or 1 integers.")
if (num_wires := len(self.wires)) != len(prep_vals):
raise ValueError("BasisState parameter and wires must be of equal length.")
prep_vals = math.cast(prep_vals, int)
if wire_order is None:
indices = prep_vals
else:
if not Wires(wire_order).contains_wires(self.wires):
raise WireError("Custom wire_order must contain all BasisState wires")
num_wires = len(wire_order)
indices = [0] * num_wires
for base_wire_label, value in zip(self.wires, prep_vals):
indices[wire_order.index(base_wire_label)] = value
ket = np.zeros((2,) * num_wires)
ket[tuple(indices)] = 1
return math.convert_like(ket, prep_vals)
[docs]class StatePrep(StatePrepBase):
r"""StatePrep(state, wires)
Prepare subsystems using the given ket vector in the computational basis.
**Details:**
* Number of wires: Any (the operation can act on any number of wires)
* Number of parameters: 1
* Gradient recipe: None
.. note::
If the ``StatePrep`` operation is not supported natively on the
target device, PennyLane will attempt to decompose the operation
using the method developed by Möttönen et al. (Quantum Info. Comput.,
2005).
.. note::
When called in the middle of a circuit, the action of the operation is defined
as :math:`U|0\rangle = |\psi\rangle`
Args:
state (array[complex]): a state vector of size 2**len(wires)
wires (Sequence[int] or int): the wire(s) the operation acts on
id (str): custom label given to an operator instance,
can be useful for some applications where the instance has to be identified.
**Example**
>>> dev = qml.device('default.qubit', wires=2)
>>> @qml.qnode(dev)
... def example_circuit():
... qml.StatePrep(np.array([1, 0, 0, 0]), wires=range(2))
... return qml.state()
>>> print(example_circuit())
[1.+0.j 0.+0.j 0.+0.j 0.+0.j]
"""
num_wires = AnyWires
num_params = 1
"""int: Number of trainable parameters that the operator depends on."""
ndim_params = (1,)
"""int: Number of dimensions per trainable parameter of the operator."""
def __init__(self, state, wires, id=None):
super().__init__(state, wires=wires, id=id)
state = self.parameters[0]
if len(state.shape) == 1:
state = math.reshape(state, (1, state.shape[0]))
if state.shape[1] != 2 ** len(self.wires):
raise ValueError("State vector must have shape (2**wires,) or (batch_size, 2**wires).")
param = math.cast(state, np.complex128)
if not math.is_abstract(param):
norm = math.linalg.norm(param, axis=-1, ord=2)
if not math.allclose(norm, 1.0, atol=1e-10):
raise ValueError("Sum of amplitudes-squared does not equal one.")
[docs] @staticmethod
def compute_decomposition(state, wires):
r"""Representation of the operator as a product of other operators (static method). :
.. math:: O = O_1 O_2 \dots O_n.
.. seealso:: :meth:`~.StatePrep.decomposition`.
Args:
state (array[complex]): a state vector of size 2**len(wires)
wires (Iterable, Wires): the wire(s) the operation acts on
Returns:
list[Operator]: decomposition into lower level operations
**Example:**
>>> qml.StatePrep.compute_decomposition(np.array([1, 0, 0, 0]), wires=range(2))
[MottonenStatePreparation(tensor([1, 0, 0, 0], requires_grad=True), wires=[0, 1])]
"""
return [MottonenStatePreparation(state, wires)]
[docs] def state_vector(self, wire_order=None):
num_op_wires = len(self.wires)
op_vector_shape = (-1,) + (2,) * num_op_wires if self.batch_size else (2,) * num_op_wires
op_vector = math.reshape(self.parameters[0], op_vector_shape)
if wire_order is None or Wires(wire_order) == self.wires:
return op_vector
wire_order = Wires(wire_order)
if not wire_order.contains_wires(self.wires):
raise WireError(f"Custom wire_order must contain all {self.name} wires")
# add zeros for each wire that isn't being set
extra_wires = Wires(set(wire_order) - set(self.wires))
for _ in extra_wires:
op_vector = math.stack([op_vector, math.zeros_like(op_vector)], axis=-1)
# transpose from operator wire order to provided wire order
current_wires = self.wires + extra_wires
transpose_axes = [current_wires.index(w) for w in wire_order]
if self.batch_size:
transpose_axes = [0] + [a + 1 for a in transpose_axes]
return math.transpose(op_vector, transpose_axes)
# pylint: disable=missing-class-docstring
[docs]class QubitDensityMatrix(Operation):
r"""QubitDensityMatrix(state, wires)
Prepare subsystems using the given density matrix.
If not all the wires are specified, remaining dimension is filled by :math:`\mathrm{tr}_{in}(\rho)`,
where :math:`\rho` is the full system density matrix before this operation and :math:`\mathrm{tr}_{in}` is a
partial trace over the subsystem to be replaced by input state.
**Details:**
* Number of wires: Any (the operation can act on any number of wires)
* Number of parameters: 1
* Gradient recipe: None
.. note::
Exception raised if the ``QubitDensityMatrix`` operation is not supported natively on the
target device.
Args:
state (array[complex]): a density matrix of size ``(2**len(wires), 2**len(wires))``
wires (Sequence[int] or int): the wire(s) the operation acts on
id (str): custom label given to an operator instance,
can be useful for some applications where the instance has to be identified.
.. details::
:title: Usage Details
Example:
.. code-block:: python
import pennylane as qml
nr_wires = 2
rho = np.zeros((2 ** nr_wires, 2 ** nr_wires), dtype=np.complex128)
rho[0, 0] = 1 # initialize the pure state density matrix for the |0><0| state
dev = qml.device("default.mixed", wires=2)
@qml.qnode(dev)
def circuit():
qml.QubitDensityMatrix(rho, wires=[0, 1])
return qml.state()
Running this circuit:
>>> circuit()
[[1.+0.j 0.+0.j 0.+0.j 0.+0.j]
[0.+0.j 0.+0.j 0.+0.j 0.+0.j]
[0.+0.j 0.+0.j 0.+0.j 0.+0.j]
[0.+0.j 0.+0.j 0.+0.j 0.+0.j]]
"""
num_wires = AnyWires
num_params = 1
"""int: Number of trainable parameters that the operator depends on."""
grad_method = None
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