Source code for pennylane.templates.layers.cv_neural_net

# Copyright 2018-2021 Xanadu Quantum Technologies Inc.

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


[docs]class CVNeuralNetLayers(Operation): r"""A sequence of layers of a continuous-variable quantum neural network, as specified in `Killoran et al. (2019) <https://doi.org/10.1103/PhysRevResearch.1.033063>`_. The layer consists of interferometers, displacement and squeezing gates mimicking the linear transformation of a neural network in the x-basis of the quantum system, and uses a Kerr gate to introduce a 'quantum' nonlinearity. The layers act on the :math:`M` modes given in ``wires``, and include interferometers of :math:`K=M(M-1)/2` beamsplitters. The different weight parameters contain the weights for each layer. The number of layers :math:`L` is therefore derived from the first dimension of ``weights``. This example shows a 4-mode CVNeuralNet layer with squeezing gates :math:`S`, displacement gates :math:`D` and Kerr gates :math:`K`. The two big blocks are interferometers of type :mod:`pennylane.Interferometer`: .. figure:: ../../_static/layer_cvqnn.png :align: center :width: 60% :target: javascript:void(0); .. note:: The CV neural network architecture includes :class:`~pennylane.ops.Kerr` operations. Make sure to use a suitable device, such as the :code:`strawberryfields.fock` device of the `PennyLane-SF <https://github.com/XanaduAI/pennylane-sf>`_ plugin. Args: theta_1 (tensor_like): shape :math:`(L, K)` tensor of transmittivity angles for first interferometer phi_1 (tensor_like): shape :math:`(L, K)` tensor of phase angles for first interferometer varphi_1 (tensor_like): shape :math:`(L, M)` tensor of rotation angles to apply after first interferometer r (tensor_like): shape :math:`(L, M)` tensor of squeezing amounts for :class:`~pennylane.ops.Squeezing` operations phi_r (tensor_like): shape :math:`(L, M)` tensor of squeezing angles for :class:`~pennylane.ops.Squeezing` operations theta_2 (tensor_like): shape :math:`(L, K)` tensor of transmittivity angles for second interferometer phi_2 (tensor_like): shape :math:`(L, K)` tensor of phase angles for second interferometer varphi_2 (tensor_like): shape :math:`(L, M)` tensor of rotation angles to apply after second interferometer a (tensor_like): shape :math:`(L, M)` tensor of displacement magnitudes for :class:`~pennylane.ops.Displacement` operations phi_a (tensor_like): shape :math:`(L, M)` tensor of displacement angles for :class:`~pennylane.ops.Displacement` operations k (tensor_like): shape :math:`(L, M)` tensor of kerr parameters for :class:`~pennylane.ops.Kerr` operations wires (Iterable): wires that the template acts on .. details:: :title: Usage Details **Parameter shapes** A list of shapes for the 11 input parameter tensors can be computed by the static method :meth:`~.CVNeuralNetLayers.shape` and used when creating randomly initialised weights: .. code-block:: python shapes = CVNeuralNetLayers.shape(n_layers=2, n_wires=2) weights = [np.random.random(shape) for shape in shapes] def circuit(): CVNeuralNetLayers(*weights, wires=[0, 1]) return qml.expval(qml.QuadX(0)) """ num_wires = AnyWires grad_method = None def __init__( self, theta_1, phi_1, varphi_1, r, phi_r, theta_2, phi_2, varphi_2, a, phi_a, k, wires, id=None, ): n_wires = len(wires) # n_if -> theta and phi shape for Interferometer n_if = n_wires * (n_wires - 1) // 2 # check that first dimension is the same weights_list = [theta_1, phi_1, varphi_1, r, phi_r, theta_2, phi_2, varphi_2, a, phi_a, k] shapes = [qml.math.shape(w) for w in weights_list] first_dims = [s[0] for s in shapes] if len(set(first_dims)) > 1: raise ValueError( f"The first dimension of all parameters needs to be the same, got {first_dims}" ) # check second dimensions second_dims = [s[1] for s in shapes] expected = [n_if] * 2 + [n_wires] * 3 + [n_if] * 2 + [n_wires] * 4 if not all(e == d for e, d in zip(expected, second_dims)): raise ValueError("Got unexpected shape for one or more parameters.") self.n_layers = shapes[0][0] super().__init__( theta_1, phi_1, varphi_1, r, phi_r, theta_2, phi_2, varphi_2, a, phi_a, k, wires=wires, id=id, ) @property def num_params(self): return 11
[docs] @staticmethod def compute_decomposition( theta_1, phi_1, varphi_1, r, phi_r, theta_2, phi_2, varphi_2, a, phi_a, k, 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:`~.CVNeuralNetLayers.decomposition`. Args: theta_1 (tensor_like): shape :math:`(L, K)` tensor of transmittivity angles for first interferometer phi_1 (tensor_like): shape :math:`(L, K)` tensor of phase angles for first interferometer varphi_1 (tensor_like): shape :math:`(L, M)` tensor of rotation angles to apply after first interferometer r (tensor_like): shape :math:`(L, M)` tensor of squeezing amounts for :class:`~pennylane.ops.Squeezing` operations phi_r (tensor_like): shape :math:`(L, M)` tensor of squeezing angles for :class:`~pennylane.ops.Squeezing` operations theta_2 (tensor_like): shape :math:`(L, K)` tensor of transmittivity angles for second interferometer phi_2 (tensor_like): shape :math:`(L, K)` tensor of phase angles for second interferometer varphi_2 (tensor_like): shape :math:`(L, M)` tensor of rotation angles to apply after second interferometer a (tensor_like): shape :math:`(L, M)` tensor of displacement magnitudes for :class:`~pennylane.ops.Displacement` operations phi_a (tensor_like): shape :math:`(L, M)` tensor of displacement angles for :class:`~pennylane.ops.Displacement` operations k (tensor_like): shape :math:`(L, M)` tensor of kerr parameters for :class:`~pennylane.ops.Kerr` operations wires (Any or Iterable[Any]): wires that the operator acts on Returns: list[.Operator]: decomposition of the operator **Example** >>> theta_1 = torch.tensor([[0.4]]) >>> phi_1 = torch.tensor([[-0.3]]) >>> varphi_1 = = torch.tensor([[1.7, 0.1]]) >>> r = torch.tensor([[-1., -0.2]]) >>> phi_r = torch.tensor([[0.2, -0.2]]) >>> theta_2 = torch.tensor([[1.4]]) >>> phi_2 = torch.tensor([[-0.4]]) >>> varphi_2 = torch.tensor([[0.1, 0.2]]) >>> a = torch.tensor([[0.1, 0.2]]) >>> phi_a = torch.tensor([[-1.1, 0.2]]) >>> k = torch.tensor([[0.1, 0.2]]) >>> qml.CVNeuralNetLayers.compute_decomposition(theta_1, phi_1, varphi_1, r, phi_r, theta_2, ... phi_2, varphi_2, a, phi_a, k, wires=["a", "b"]) [Interferometer(tensor([0.4000]), tensor([-0.3000]), tensor([1.7000, 0.1000]), wires=['a', 'b']), Squeezing(tensor(-1.), tensor(0.2000), wires=['a']), Squeezing(tensor(-0.2000), tensor(-0.2000), wires=['b']), Interferometer(tensor([1.4000]), tensor([-0.4000]), tensor([0.1000, 0.2000]), wires=['a', 'b']), Displacement(tensor(0.1000), tensor(-1.1000), wires=['a']), Displacement(tensor(0.2000), tensor(0.2000), wires=['b']), Kerr(tensor(0.1000), wires=['a']), Kerr(tensor(0.2000), wires=['b'])] """ op_list = [] n_layers = qml.math.shape(theta_1)[0] for m in range(n_layers): op_list.append( qml.Interferometer( theta=theta_1[m], phi=phi_1[m], varphi=varphi_1[m], wires=wires, ) ) for i in range(len(wires)): # pylint:disable=consider-using-enumerate op_list.append(qml.Squeezing(r[m, i], phi_r[m, i], wires=wires[i])) op_list.append( qml.Interferometer( theta=theta_2[m], phi=phi_2[m], varphi=varphi_2[m], wires=wires, ) ) for i in range(len(wires)): # pylint: disable=consider-using-enumerate op_list.append(qml.Displacement(a[m, i], phi_a[m, i], wires=wires[i])) for i in range(len(wires)): # pylint:disable=consider-using-enumerate op_list.append(qml.Kerr(k[m, i], wires=wires[i])) return op_list
[docs] @staticmethod def shape(n_layers, n_wires): r"""Returns a list of shapes for the 11 parameter tensors. Args: n_layers (int): number of layers n_wires (int): number of wires Returns: list[tuple[int]]: list of shapes """ # n_if -> theta and phi shape for Interferometer n_if = n_wires * (n_wires - 1) // 2 shapes = ( [(n_layers, n_if)] * 2 + [(n_layers, n_wires)] * 3 + [(n_layers, n_if)] * 2 + [(n_layers, n_wires)] * 4 ) return shapes