Source code for pennylane.templates.embeddings.squeezing

# Copyright 2018-2021 Xanadu Quantum Technologies Inc.

# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0

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r"""
Contains the SqueezingEmbedding template.
"""
# pylint: disable-msg=too-many-branches,too-many-arguments,protected-access
import pennylane as qml
from pennylane.operation import AnyWires, Operation


[docs]class SqueezingEmbedding(Operation): r"""Encodes :math:`N` features into the squeezing amplitudes :math:`r \geq 0` or phases :math:`\phi \in [0, 2\pi)` of :math:`M` modes, where :math:`N\leq M`. The mathematical definition of the squeezing gate is given by the operator .. math:: S(z) = \exp\left(\frac{r}{2}\left(e^{-i\phi}\a^2 -e^{i\phi}{\ad}^{2} \right) \right), where :math:`\a` and :math:`\ad` are the bosonic creation and annihilation operators. ``features`` has to be an iterable of at most ``len(wires)`` floats. If there are fewer entries in ``features`` than wires, the circuit does not apply the remaining squeezing gates. Args: features (tensor_like): tensor of features wires (Any or Iterable[Any]): wires that the template acts on method (str): ``'phase'`` encodes the input into the phase of single-mode squeezing, while ``'amplitude'`` uses the amplitude c (float): value of the phase of all squeezing gates if ``execution='amplitude'``, or the amplitude of all squeezing gates if ``execution='phase'`` Raises: ValueError: if inputs do not have the correct format Example: Depending on the ``method`` argument, the feature vector will be encoded in the phase or the amplitude. The argument ``c`` will define the value of the other quantity. The default values are :math:`0.1` for ``c`` and ``'amplitude'`` for ``method``. .. code-block:: python dev = qml.device('default.gaussian', wires=3) @qml.qnode(dev) def circuit(feature_vector): qml.SqueezingEmbedding(features=feature_vector, wires=range(3)) qml.QuadraticPhase(0.1, wires=1) return qml.expval(qml.NumberOperator(wires=1)) X = [1, 2, 3] >>> print(circuit(X)) 13.018280763205285 And, the resulting circuit is: >>> print(qml.draw(circuit, show_matrices=False)(X)) 0: ─╭SqueezingEmbedding(M0)──────────┤ 1: ─├SqueezingEmbedding(M0)──P(0.10)─┤ <n> 2: ─╰SqueezingEmbedding(M0)──────────┤ Using different parameters: .. code-block:: python dev = qml.device('default.gaussian', wires=3) @qml.qnode(dev) def circuit(feature_vector): qml.SqueezingEmbedding(features=feature_vector, wires=range(3), method='phase', c=0.5) qml.QuadraticPhase(0.1, wires=1) return qml.expval(qml.NumberOperator(wires=1)) X = [1, 2, 3] >>> print(circuit(X)) 0.22319028857312428 And, the resulting circuit is: >>> print(qml.draw(circuit, show_matrices=False)(X)) 0: ─╭SqueezingEmbedding(M0)──────────┤ 1: ─├SqueezingEmbedding(M0)──P(0.10)─┤ <n> 2: ─╰SqueezingEmbedding(M0)──────────┤ """ num_wires = AnyWires grad_method = None @classmethod def _unflatten(cls, data, metadata) -> "SqueezingEmbedding": new_op = cls.__new__(cls) Operation.__init__(new_op, *data, wires=metadata[0]) return new_op def __init__(self, features, wires, method="amplitude", c=0.1, id=None): shape = qml.math.shape(features) constants = [c] * shape[0] constants = qml.math.convert_like(constants, features) if len(shape) != 1: raise ValueError(f"Features must be a one-dimensional tensor; got shape {shape}.") n_features = shape[0] if n_features != len(wires): raise ValueError(f"Features must be of length {len(wires)}; got length {n_features}.") if method == "amplitude": pars = qml.math.stack([features, constants], axis=1) elif method == "phase": pars = qml.math.stack([constants, features], axis=1) else: raise ValueError(f"did not recognize method {method}") super().__init__(pars, wires=wires, id=id) @property def num_params(self): return 1
[docs] @staticmethod def compute_decomposition(pars, wires): # pylint: disable=arguments-differ r"""Representation of the operator as a product of other operators. .. math:: O = O_1 O_2 \dots O_n. .. seealso:: :meth:`~.SqueezingEmbedding.decomposition`. Args: pars (tensor_like): parameters extracted from features and constant wires (Any or Iterable[Any]): wires that the operator acts on Returns: list[.Operator]: decomposition of the operator **Example** >>> pars = torch.tensor([[1., 0.], [2., 0.]]) >>> qml.SqueezingEmbedding.compute_decomposition(pars, wires=["a", "b"]) [Squeezing(tensor(1.), tensor(0.), wires=['a']), Squeezing(tensor(2.), tensor(0.), wires=['b'])] """ return [ qml.Squeezing(pars[i, 0], pars[i, 1], wires=wires[i : i + 1]) for i in range(len(wires)) ]