def cv_neural_net_layer(theta_1, phi_1, varphi_1, r, phi_r, theta_2, phi_2, varphi_2, a, phi_a, k, wires): r"""A single continuous-variable neural network layer. The layer acts on the :math:`M` wires modes specified in ``wires``, and includes interferometers of :math:`K=M(M-1)/2` beamsplitters. Args: theta_1 (array[float]): length :math:`(K, )` array of transmittivity angles for first interferometer phi_1 (array[float]): length :math:`(K, )` array of phase angles for first interferometer varphi_1 (array[float]): length :math:`(M, )` array of rotation angles to apply after first interferometer r (array[float]): length :math:`(M, )` array of squeezing amounts for :class:`~pennylane.ops.Squeezing` operations phi_r (array[float]): length :math:`(M, )` array of squeezing angles for :class:`~pennylane.ops.Squeezing` operations theta_2 (array[float]): length :math:`(K, )` array of transmittivity angles for second interferometer phi_2 (array[float]): length :math:`(K, )` array of phase angles for second interferometer varphi_2 (array[float]): length :math:`(M, )` array of rotation angles to apply after second interferometer a (array[float]): length :math:`(M, )` array of displacement magnitudes for :class:`~pennylane.ops.Displacement` operations phi_a (array[float]): length :math:`(M, )` array of displacement angles for :class:`~pennylane.ops.Displacement` operations k (array[float]): length :math:`(M, )` array of kerr parameters for :class:`~pennylane.ops.Kerr` operations wires (Sequence[int]): sequence of mode indices that the template acts on """ Interferometer(theta=theta_1, phi=phi_1, varphi=varphi_1, wires=wires) for i, wire in enumerate(wires): Squeezing(r[i], phi_r[i], wires=wire) Interferometer(theta=theta_2, phi=phi_2, varphi=varphi_2, wires=wires) for i, wire in enumerate(wires): Displacement(a[i], phi_a[i], wires=wire) for i, wire in enumerate(wires): Kerr(k[i], wires=wire)
def test_three_mode(self, tol): """Test that a three mode interferometer using either mesh gives the correct gates""" N = 3 wires = range(N) theta = [0.321, 0.4523, 0.21321] phi = [0.234, 0.324, 0.234] varphi = [0.42342, 0.234, 0.1121] with qml.utils.OperationRecorder() as rec_rect: Interferometer(theta, phi, varphi, wires=wires) with qml.utils.OperationRecorder() as rec_tria: Interferometer(theta, phi, varphi, wires=wires) for rec in [rec_rect, rec_tria]: # test both meshes (both give identical results for the 3 mode case). assert len(rec.queue) == 6 expected_bs_wires = [[0, 1], [1, 2], [0, 1]] for idx, op in enumerate(rec_rect.queue[:3]): assert isinstance(op, qml.Beamsplitter) assert op.parameters == [theta[idx], phi[idx]] assert op.wires == expected_bs_wires[idx] for idx, op in enumerate(rec.queue[3:]): assert isinstance(op, qml.Rotation) assert op.parameters == [varphi[idx]] assert op.wires == [idx]
def cv_neural_net_layer( theta_1, phi_1, varphi_1, r, phi_r, theta_2, phi_2, varphi_2, a, phi_a, k, wires): # unitary transformation Interferometer(theta=theta_1, phi=phi_1, varphi=varphi_1, wires=wires) # scaling broadcast(unitary=Squeezing, pattern="single", wires=wires, parameters=list(zip(r, phi_r))) # unitary transformation Interferometer(theta=theta_2, phi=phi_2, varphi=varphi_2, wires=wires) # bias broadcast(unitary=Displacement, pattern="single", wires=wires, parameters=list(zip(a, phi_a))) # non-linearity broadcast(unitary=Kerr, pattern="single", wires=wires, parameters=k)
def test_clements_beamsplitter_convention(self, tol): """test the beamsplitter convention""" N = 2 wires = range(N) theta = [0.321] phi = [0.234] varphi = [0.42342, 0.1121] with qml.utils.OperationRecorder() as rec_rect: Interferometer(theta, phi, varphi, mesh='rectangular', beamsplitter='clements', wires=wires) with qml.utils.OperationRecorder() as rec_tria: Interferometer(theta, phi, varphi, mesh='triangular', beamsplitter='clements', wires=wires) for rec in [rec_rect, rec_tria]: assert len(rec.queue) == 4 assert isinstance(rec.queue[0], qml.Rotation) assert rec.queue[0].parameters == phi assert isinstance(rec.queue[1], qml.Beamsplitter) assert rec.queue[1].parameters == [theta[0], 0] assert isinstance(rec.queue[2], qml.Rotation) assert rec.queue[2].parameters == [varphi[0]] assert isinstance(rec.queue[3], qml.Rotation) assert rec.queue[3].parameters == [varphi[1]]
def CVNeuralNetLayer(theta_1, phi_1, varphi_1, r, phi_r, theta_2, phi_2, varphi_2, a, phi_a, k, wires): r"""A layer of interferometers, displacement and squeezing gates mimicking a neural network, as well as a Kerr gate nonlinearity. The layer acts on the :math:`M` wires modes specified in ``wires``, and includes interferometers of :math:`K=M(M-1)/2` beamsplitters. 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.templates.layers.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 (array[float]): length :math:`(K, )` array of transmittivity angles for first interferometer phi_1 (array[float]): length :math:`(K, )` array of phase angles for first interferometer varphi_1 (array[float]): length :math:`(M, )` array of rotation angles to apply after first interferometer r (array[float]): length :math:`(M, )` array of squeezing amounts for :class:`~pennylane.ops.Squeezing` operations phi_r (array[float]): length :math:`(M, )` array of squeezing angles for :class:`~pennylane.ops.Squeezing` operations theta_2 (array[float]): length :math:`(K, )` array of transmittivity angles for second interferometer phi_2 (array[float]): length :math:`(K, )` array of phase angles for second interferometer varphi_2 (array[float]): length :math:`(M, )` array of rotation angles to apply after second interferometer a (array[float]): length :math:`(M, )` array of displacement magnitudes for :class:`~pennylane.ops.Displacement` operations phi_a (array[float]): length :math:`(M, )` array of displacement angles for :class:`~pennylane.ops.Displacement` operations k (array[float]): length :math:`(M, )` array of kerr parameters for :class:`~pennylane.ops.Kerr` operations wires (Sequence[int]): sequence of mode indices that the template acts on """ Interferometer(theta=theta_1, phi=phi_1, varphi=varphi_1, wires=wires) for i, wire in enumerate(wires): Squeezing(r[i], phi_r[i], wires=wire) Interferometer(theta=theta_2, phi=phi_2, varphi=varphi_2, wires=wires) for i, wire in enumerate(wires): Displacement(a[i], phi_a[i], wires=wire) for i, wire in enumerate(wires): Kerr(k[i], wires=wire)
def circuit(varphi, bs=None): Interferometer(theta=[], phi=[], varphi=varphi, beamsplitter=bs, wires=0) return qml.expval(qml.NumberOperator(0))
def test_four_mode_triangular(self, tol): """Test that a 4 mode interferometer using triangular mesh gives the correct gates""" N = 4 wires = range(N) theta = [0.321, 0.4523, 0.21321, 0.123, 0.5234, 1.23] phi = [0.234, 0.324, 0.234, 1.453, 1.42341, -0.534] varphi = [0.42342, 0.234, 0.4523, 0.1121] with qml.utils.OperationRecorder() as rec: Interferometer(theta, phi, varphi, mesh='triangular', wires=wires) assert len(rec.queue) == 10 expected_bs_wires = [[2, 3], [1, 2], [0, 1], [2, 3], [1, 2], [2, 3]] for idx, op in enumerate(rec.queue[:6]): assert isinstance(op, qml.Beamsplitter) assert op.parameters == [theta[idx], phi[idx]] assert op.wires == expected_bs_wires[idx] for idx, op in enumerate(rec.queue[6:]): assert isinstance(op, qml.Rotation) assert op.parameters == [varphi[idx]] assert op.wires == [idx]
def cv_neural_net_layer(theta_1, phi_1, varphi_1, r, phi_r, theta_2, phi_2, varphi_2, a, phi_a, k, wires): Interferometer(theta=theta_1, phi=phi_1, varphi=varphi_1, wires=wires) broadcast(unitary=Squeezing, pattern="single", wires=wires, parameters=list(zip(r, phi_r))) Interferometer(theta=theta_2, phi=phi_2, varphi=varphi_2, wires=wires) broadcast(unitary=Displacement, pattern="single", wires=wires, parameters=list(zip(a, phi_a))) broadcast(unitary=Kerr, pattern="single", wires=wires, parameters=k)
def test_one_mode(self, tol): """Test that a one mode interferometer correctly gives a rotation gate""" varphi = [0.42342] with qml.utils.OperationRecorder() as rec: Interferometer(theta=[], phi=[], varphi=varphi, wires=0) assert len(rec.queue) == 1 assert isinstance(rec.queue[0], qml.Rotation) assert np.allclose(rec.queue[0].parameters, varphi, atol=tol)
def cv_neural_net_layer(theta_1, phi_1, varphi_1, r, phi_r, theta_2, phi_2, varphi_2, a, phi_a, k, wires): r"""A single continuous-variable neural network layer. The layer acts on the :math:`M` wires modes specified in ``wires``, and includes interferometers of :math:`K=M(M-1)/2` beamsplitters. Args: theta_1 (tensor_like): shape :math:`(K, )` tensor of transmittivity angles for first interferometer phi_1 (tensor_like): shape :math:`(K, )` tensor of phase angles for first interferometer varphi_1 (tensor_like): shape :math:`(M, )` tensor of rotation angles to apply after first interferometer r (tensor_like): shape :math:`(M, )` tensor of squeezing amounts for :class:`~pennylane.ops.Squeezing` operations phi_r (tensor_like): shape :math:`(M, )` tensor of squeezing angles for :class:`~pennylane.ops.Squeezing` operations theta_2 (tensor_like): shape :math:`(K, )` tensor of transmittivity angles for second interferometer phi_2 (tensor_like): shape :math:`(K, )` tensor of phase angles for second interferometer varphi_2 (tensor_like): shape :math:`(M, )` tensor of rotation angles to apply after second interferometer a (tensor_like): shape :math:`(M, )` tensor of displacement magnitudes for :class:`~pennylane.ops.Displacement` operations phi_a (tensor_like): shape :math:`(M, )` tensor of displacement angles for :class:`~pennylane.ops.Displacement` operations k (tensor_like): shape :math:`(M, )` tensor of kerr parameters for :class:`~pennylane.ops.Kerr` operations wires (Wires): wires that the template acts on """ Interferometer(theta=theta_1, phi=phi_1, varphi=varphi_1, wires=wires) broadcast(unitary=Squeezing, pattern="single", wires=wires, parameters=list(zip(r, phi_r))) Interferometer(theta=theta_2, phi=phi_2, varphi=varphi_2, wires=wires) broadcast(unitary=Displacement, pattern="single", wires=wires, parameters=list(zip(a, phi_a))) broadcast(unitary=Kerr, pattern="single", wires=wires, parameters=k)
def test_two_mode_rect(self, tol): """Test that a two mode interferometer using the rectangular mesh correctly gives a beamsplitter+rotation gate""" N = 2 wires = range(N) theta = [0.321] phi = [0.234] varphi = [0.42342, 0.1121] with qml.utils.OperationRecorder() as rec: Interferometer(theta, phi, varphi, wires=wires) isinstance(rec.queue[0], qml.Beamsplitter) assert rec.queue[0].parameters == theta + phi assert isinstance(rec.queue[1], qml.Rotation) assert rec.queue[1].parameters == [varphi[0]] assert isinstance(rec.queue[2], qml.Rotation) assert rec.queue[2].parameters == [varphi[1]]
def circuit(varphi, mesh=None): Interferometer(theta=[], phi=[], varphi=varphi, mesh=mesh, wires=0) return qml.expval(qml.NumberOperator(0))
def circuit(theta, phi, varphi): for w in wires: qml.Squeezing(sq[w][0], sq[w][1], wires=w) Interferometer(theta=theta, phi=phi, varphi=varphi, wires=wires) return [qml.expval(qml.NumberOperator(w)) for w in wires]
def CVNeuralNetLayers( theta_1, phi_1, varphi_1, r, phi_r, theta_2, phi_2, varphi_2, a, phi_a, k, wires ): r"""A sequence of layers of a continuous-variable quantum neural network, as specified in `arXiv:1806.06871 <https://arxiv.org/abs/1806.06871>`_. 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.templates.layers.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 or Wires): Wires that the template acts on. Accepts an iterable of numbers or strings, or a Wires object. Raises: ValueError: if inputs do not have the correct format """ wires = Wires(wires) repeat = _preprocess( theta_1, phi_1, varphi_1, r, phi_r, theta_2, phi_2, varphi_2, a, phi_a, k, wires ) for l in range(repeat): Interferometer(theta=theta_1[l], phi=phi_1[l], varphi=varphi_1[l], wires=wires) r_and_phi_r = qml.math.stack([r[l], phi_r[l]], axis=1) broadcast(unitary=Squeezing, pattern="single", wires=wires, parameters=r_and_phi_r) Interferometer(theta=theta_2[l], phi=phi_2[l], varphi=varphi_2[l], wires=wires) a_and_phi_a = qml.math.stack([a[l], phi_a[l]], axis=1) broadcast(unitary=Displacement, pattern="single", wires=wires, parameters=a_and_phi_a) broadcast(unitary=Kerr, pattern="single", wires=wires, parameters=k[l])