Source code for pennylane.templates.tensornetworks.mps
# 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.
"""
Contains the MPS template.
"""
# pylint: disable-msg=too-many-branches,too-many-arguments,protected-access
import warnings
import pennylane as qml
from pennylane.operation import AnyWires, Operation
def compute_indices_MPS(wires, n_block_wires, offset=None):
r"""Generate a list containing the wires for each block.
Args:
wires (Iterable): wires that the template acts on
n_block_wires (int): number of wires per block_gen
offset (int): offset value for positioning the subsequent blocks relative to each other.
If ``None``, it defaults to :math:`\text{offset} = \lfloor \text{n_block_wires}/2 \rfloor`,
otherwise :math:`\text{offset} \in [1, \text{n_block_wires} - 1]`.
Returns:
layers (Tuple[Tuple]]): array of wire indices or wire labels for each block
"""
n_wires = len(wires)
if n_block_wires < 2:
raise ValueError(
f"The number of wires in each block must be larger than or equal to 2; got n_block_wires = {n_block_wires}"
)
if n_block_wires > n_wires:
raise ValueError(
f"n_block_wires must be smaller than or equal to the number of wires; got n_block_wires = {n_block_wires} and number of wires = {n_wires}"
)
if offset is None:
offset = n_block_wires // 2
if offset < 1 or offset > n_block_wires - 1:
raise ValueError(
f"Provided offset is outside the expected range; the expected range for n_block_wires = {n_block_wires} is range{1, n_block_wires - 1}"
)
n_step = offset
n_layers = len(wires) - int(len(wires) % (n_block_wires // 2)) - n_step
return tuple(
tuple(wires[idx] for idx in range(j, j + n_block_wires))
for j in range(
0,
n_layers,
n_step,
)
if not j + n_block_wires > len(wires)
)
[docs]class MPS(Operation):
r"""The MPS template broadcasts an input circuit across many wires following the architecture of a Matrix Product State tensor network.
The result is similar to the architecture in `arXiv:1803.11537 <https://arxiv.org/abs/1803.11537>`_.
The keyword argument ``block`` is a user-defined quantum circuit that should accept two arguments: ``wires`` and ``weights``.
The latter argument is optional in case the implementation of ``block`` doesn't require any weights. Any additional arguments
should be provided using the ``kwargs``.
Args:
wires (Iterable): wires that the template acts on
n_block_wires (int): number of wires per block
block (Callable): quantum circuit that defines a block
n_params_block (int): the number of parameters in a block; equal to the length of the ``weights`` argument in ``block``
template_weights (Sequence): list containing the weights for all blocks
offset (int): offset value for positioning the subsequent blocks relative to each other.
If ``None``, it defaults to :math:`\text{offset} = \lfloor \text{n_block_wires}/2 \rfloor`,
otherwise :math:`\text{offset} \in [1, \text{n_block_wires} - 1]`
**kwargs: additional keyword arguments for implementing the ``block``
.. note::
The expected number of blocks can be obtained from ``qml.MPS.get_n_blocks(wires, n_block_wires, offset=0)``, and
the length of ``template_weights`` argument should match the number of blocks. Whenever either ``n_block_wires``
is odd or ``offset`` is not :math:`\lfloor \text{n_block_wires}/2 \rfloor`, the template deviates from the maximally
unbalanced tree architecture described in `arXiv:1803.11537 <https://arxiv.org/abs/1803.11537>`_.
.. details::
:title: Usage Details
This example demonstrates the use of ``MPS`` for a simple block.
.. code-block:: python
import pennylane as qml
import numpy as np
def block(weights, wires):
qml.CNOT(wires=[wires[0],wires[1]])
qml.RY(weights[0], wires=wires[0])
qml.RY(weights[1], wires=wires[1])
n_wires = 4
n_block_wires = 2
n_params_block = 2
n_blocks = qml.MPS.get_n_blocks(range(n_wires),n_block_wires)
template_weights = [[0.1, -0.3]] * n_blocks
dev= qml.device('default.qubit',wires=range(n_wires))
@qml.qnode(dev)
def circuit(template_weights):
qml.MPS(range(n_wires),n_block_wires,block, n_params_block, template_weights)
return qml.expval(qml.Z(n_wires-1))
>>> print(qml.draw(circuit, level='device')(template_weights))
0: ─╭●──RY(0.10)──────────────────────────────┤
1: ─╰X──RY(-0.30)─╭●──RY(0.10)────────────────┤
2: ───────────────╰X──RY(-0.30)─╭●──RY(0.10)──┤
3: ─────────────────────────────╰X──RY(-0.30)─┤ <Z>
MPS can also be used with an ``offset`` argument that shifts the positioning the subsequent blocks from the default ``n_block_wires/2``.
.. code-block:: python
import pennylane as qml
import numpy as np
def block(wires):
qml.MultiControlledX(wires=[wires[i] for i in range(len(wires))])
n_wires = 8
n_block_wires = 4
n_params_block = 2
dev= qml.device('default.qubit',wires=n_wires)
@qml.qnode(dev)
def circuit():
qml.MPS(range(n_wires),n_block_wires, block, n_params_block, offset = 1)
return qml.state()
>>> print(qml.draw(circuit, level='device')())
0: ─╭●─────────────┤ State
1: ─├●─╭●──────────┤ State
2: ─├●─├●─╭●───────┤ State
3: ─╰X─├●─├●─╭●────┤ State
4: ────╰X─├●─├●─╭●─┤ State
5: ───────╰X─├●─├●─┤ State
6: ──────────╰X─├●─┤ State
7: ─────────────╰X─┤ State
"""
num_wires = AnyWires
par_domain = "A"
@classmethod
def _primitive_bind_call(
cls,
wires,
n_block_wires,
block,
n_params_block,
template_weights=None,
offset=None,
id=None,
**kwargs,
): # pylint: disable=arguments-differ
return super()._primitive_bind_call(
wires=wires,
n_block_wires=n_block_wires,
block=block,
n_params_block=n_params_block,
template_weights=template_weights,
id=id,
offset=offset,
**kwargs,
)
@classmethod
def _unflatten(cls, data, metadata):
new_op = cls.__new__(cls)
setattr(new_op, "_hyperparameters", dict(metadata[1]))
setattr(new_op, "_weights", data[0] if len(data) else None)
Operation.__init__(new_op, *data, wires=metadata[0])
return new_op
def __init__(
self,
wires,
n_block_wires,
block,
n_params_block=0,
template_weights=None,
offset=None,
id=None,
**kwargs,
):
ind_gates = compute_indices_MPS(wires, n_block_wires, offset)
n_blocks = self.get_n_blocks(wires, n_block_wires, offset)
if template_weights is not None:
shape = qml.math.shape(template_weights) # (n_blocks, n_params_block)
if shape[0] != n_blocks:
raise ValueError(
f"Weights tensor must have first dimension of length {n_blocks}; got {shape[0]}"
)
if shape[-1] != n_params_block:
raise ValueError(
f"Weights tensor must have last dimension of length {n_params_block}; got {shape[-1]}"
)
self._weights = template_weights
self._hyperparameters = {"ind_gates": ind_gates, "block": block, **kwargs}
if self._weights is None:
super().__init__(wires=wires, id=id)
else:
super().__init__(self._weights, wires=wires, id=id)
@property
def num_params(self):
"""int: Number of trainable parameters that the operator depends on."""
return 0 if self._weights is None else 1
[docs] @staticmethod
def compute_decomposition(
weights=None, wires=None, ind_gates=None, block=None, **kwargs
): # pylint: disable=arguments-differ,unused-argument
r"""Representation of the operator as a product of other operators.
.. math:: O = O_1 O_2 \dots O_n.
.. seealso:: :meth:`~.MPS.decomposition`.
Args:
weights (list[tensor_like]): list containing the weights for all blocks
wires (Iterable): wires that the template acts on
block (Callable): quantum circuit that defines a block
ind_gates (array): array of wire indices
**kwargs: additional keyword arguments for implementing the ``block``
Returns:
list[.Operator]: decomposition of the operator
"""
decomp = []
itrweights = iter([]) if weights is None else iter(weights)
block_gen = qml.tape.make_qscript(block)
for w in ind_gates:
weight = next(itrweights, None)
decomp += (
block_gen(wires=w, **kwargs)
if weight is None
else block_gen(weights=weight, wires=w, **kwargs)
)
return [qml.apply(op) for op in decomp] if qml.QueuingManager.recording() else decomp
[docs] @staticmethod
def get_n_blocks(wires, n_block_wires, offset=None):
r"""Returns the expected number of blocks for a set of wires and number of wires per block.
Args:
wires (Sequence): number of wires the template acts on
n_block_wires (int): number of wires per block
offset (int): offset value for positioning the subsequent blocks relative to each other.
If ``None``, it defaults to :math:`\text{offset} = \lfloor \text{n_block_wires}/2 \rfloor`,
otherwise :math:`\text{offset} \in [1, \text{n_block_wires} - 1]`.
Returns:
n_blocks (int): number of blocks; expected length of the template_weights argument
"""
n_wires = len(wires)
if offset is None and not n_block_wires % 2 and n_wires % (n_block_wires // 2) > 0:
warnings.warn(
f"The number of wires should be a multiple of {int(n_block_wires/2)}; got {n_wires}"
)
if n_block_wires > n_wires:
raise ValueError(
f"n_block_wires must be smaller than or equal to the number of wires; got n_block_wires = {n_block_wires} and number of wires = {n_wires}"
)
if offset is None:
offset = n_block_wires // 2
if offset < 1 or offset > n_block_wires - 1:
raise ValueError(
f"Provided offset is outside the expected range; the expected range for n_block_wires = {n_block_wires} is range{1, n_block_wires - 1}"
)
n_step = offset
n_layers = n_wires - int(n_wires % (n_block_wires // 2)) - n_step
return len([idx for idx in range(0, n_layers, n_step) if not idx + n_block_wires > n_wires])
_modules/pennylane/templates/tensornetworks/mps
Download Python script
Download Notebook
View on GitHub