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
import pennylane.numpy as np
from pennylane.operation import Operation, AnyWires
def compute_indices_MPS(wires, n_block_wires):
"""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
Returns:
layers (Tuple[Tuple]]): array of wire indices or wire labels for each block
"""
n_wires = len(wires)
if n_block_wires % 2 != 0:
raise ValueError(f"n_block_wires must be an even integer; got {n_block_wires}")
if n_block_wires < 2:
raise ValueError(
f"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 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}"
)
return tuple(
tuple(wires[idx] for idx in range(j, j + n_block_wires))
for j in range(
0,
len(wires) - int(len(wires) % (n_block_wires // 2)) - n_block_wires // 2,
n_block_wires // 2,
)
)
[docs]class MPS(Operation):
"""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 argument ``block`` is a user-defined quantum circuit.``block`` should have two arguments: ``weights`` and ``wires``.
For clarity, it is recommended to use a one-dimensional list or array for the block weights.
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
.. note::
The expected number of blocks can be obtained from ``qml.MPS.get_n_blocks(wires, n_block_wires)``.
The length of ``template_weights`` argument should match the number of blocks.
.. 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.PauliZ(wires=n_wires-1))
>>> print(qml.draw(circuit, expansion_strategy='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>
"""
num_params = 1
"""int: Number of trainable parameters that the operator depends on."""
num_wires = AnyWires
par_domain = "A"
@classmethod
def _unflatten(cls, data, metadata):
new_op = cls.__new__(cls)
new_op._hyperparameters = dict(metadata[1])
Operation.__init__(new_op, data, wires=metadata[0])
return new_op
def __init__(
self,
wires,
n_block_wires,
block,
n_params_block,
template_weights=None,
id=None,
):
ind_gates = compute_indices_MPS(wires, n_block_wires)
n_wires = len(wires)
n_blocks = int(n_wires / (n_block_wires / 2) - 1)
if template_weights is None:
template_weights = np.random.rand(n_params_block, int(n_blocks))
else:
shape = qml.math.shape(template_weights)[-4:] # (n_params_block, n_blocks)
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._hyperparameters = {"ind_gates": ind_gates, "block": block}
super().__init__(template_weights, wires=wires, id=id)
[docs] @staticmethod
def compute_decomposition(
weights, wires, ind_gates, block
): # 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
Returns:
list[.Operator]: decomposition of the operator
"""
decomp = []
block_gen = qml.tape.make_qscript(block)
for idx, w in enumerate(ind_gates):
decomp += block_gen(weights=weights[idx][:], wires=w)
return [qml.apply(op) for op in decomp] if qml.QueuingManager.recording() else decomp
[docs] @staticmethod
def get_n_blocks(wires, n_block_wires):
"""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
Returns:
n_blocks (int): number of blocks; expected length of the template_weights argument
"""
n_wires = len(wires)
if 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}"
)
n_blocks = int(n_wires / (n_block_wires / 2) - 1)
return n_blocks
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