Is this hybrid quantum-classical PINN code correct for solving the Lorenz system?

[AHDMarwan]

Hello,

I have written a hybrid quantum-classical neural network (PINN) using TensorFlow, DeepXDE, and PennyLane to solve the Lorenz system. The code integrates a quantum layer in a classical feed-forward neural network to model the system of differential equations. However, I would appreciate feedback on whether the approach is correct and if there are any improvements or issues in the implementation.

Here’s an overview of the code:

Lorenz System: A system of differential equations modeled using DeepXDE. Neural Network: Combines classical feed-forward layers with a quantum layer implemented using PennyLane. Training: The model is trained using the Adam optimizer, followed by the L-BFGS optimizer. Goal: Solve the Lorenz system and learn its parameters (C1, C2, C3). Code Snippet: ` import deepxde as dde import numpy as np import tensorflow as tf import pennylane as qml

C1 = dde.Variable(1.0) C2 = dde.Variable(1.0) C3 = dde.Variable(1.0)

n_qubits = 2 dev = qml.device("default.qubit", wires=n_qubits)

class QuantumLayer(tf.keras.layers.Layer): def init(self, **kwargs): super(QuantumLayer, self).init(**kwargs) self.weight_shape = (n_qubits, n_qubits)

def build(self, input_shape):
    self.quantum_weights = self.add_weight(
        shape=self.weight_shape,
        initializer="random_normal",
        trainable=True,
        name="quantum_weights"
    )

def call(self, inputs):
    @qml.qnode(dev, interface='tensorflow')
    def quantum_circuit(inputs, weights):
        qml.templates.AngleEmbedding(inputs, wires=range(n_qubits))
        qml.templates.BasicEntanglerLayers(weights, wires=range(n_qubits))
        return [qml.expval(qml.PauliZ(i)) for i in range(n_qubits)]

    output = tf.convert_to_tensor(quantum_circuit(inputs, self.quantum_weights))
    return tf.reshape(output, (-1, 2))

def Lorenz_system(x, y): y1, y2, y3 = y[:, 0:1], y[:, 1:2], y[:, 2:] dy1_x = dde.grad.jacobian(y, x, i=0) dy2_x = dde.grad.jacobian(y, x, i=1) dy3_x = dde.grad.jacobian(y, x, i=2) return [ dy1_x - C1 * (y2 - y1), dy2_x - y1 * (C2 - y3) + y2, dy3_x - y1 * y2 + C3 * y3, ]

def boundary(_, on_initial): return on_initial

geom = dde.geometry.TimeDomain(0, 3)

ic1 = dde.icbc.IC(geom, lambda X: -8, boundary, component=0) ic2 = dde.icbc.IC(geom, lambda X: 7, boundary, component=1) ic3 = dde.icbc.IC(geom, lambda X: 27, boundary, component=2)

observe_t, ob_y = np.load("/content/drive/MyDrive/Colab Notebooks/dataset/Lorenz.npz")["t"], np.load("/content/drive/MyDrive/Colab Notebooks/dataset/Lorenz.npz")["y"] observe_y0 = dde.icbc.PointSetBC(observe_t, ob_y[:, 0:1], component=0) observe_y1 = dde.icbc.PointSetBC(observe_t, ob_y[:, 1:2], component=1) observe_y2 = dde.icbc.PointSetBC(observe_t, ob_y[:, 2:3], component=2)

data = dde.data.PDE( geom, Lorenz_system, [ic1, ic2, ic3, observe_y0, observe_y1, observe_y2], num_domain=400, num_boundary=2, anchors=observe_t, )

layer_sizes = [1, 40, 40, 40, 3] activation = "tanh" initializer = "Glorot uniform"

class PINNWithQuantumLayer(dde.nn.FNN): def init(self, layer_sizes, activation, initializer): super().init(layer_sizes, activation, initializer) self.quantum_layer = QuantumLayer()

def call(self, inputs):
    x = super().call(inputs)
    x = self.quantum_layer(x)
    return x

net = PINNWithQuantumLayer(layer_sizes, activation, initializer)

model = dde.Model(data, net)

external_trainable_variables = [C1, C2, C3] variable = dde.callbacks.VariableValue( external_trainable_variables, period=600, filename="variables.dat" )

model.compile( "adam", lr=0.001, external_trainable_variables=external_trainable_variables ) losshistory, train_state = model.train(iterations=20000, callbacks=[variable])

model.compile("L-BFGS", external_trainable_variables=external_trainable_variables) losshistory, train_state = model.train(callbacks=[variable])

dde.saveplot(losshistory, train_state, issave=True, isplot=True) `

I would appreciate it if someone could verify:

Is the integration of the quantum layer with the classical network correct? Does the Lorenz system PDE implementation match the expected formulation? Are there any potential issues with training using both Adam and L-BFGS optimizers? Thank you!