Cloud Based Quantum Machine Learning Software -

class HybridModel(torch.nn.Module): def (self): super(). init () self.fc1 = torch.nn.Linear(4, 4) self.qnode = quantum_feature_extractor def forward(self, x): x = self.fc1(x) x = torch.tensor(self.qnode(x), requires_grad=True) return x # deep quantum features

@qml.qnode(dev, interface="torch") def quantum_feature_extractor(x): qml.AngleEmbedding(x, wires=range(4)) qml.BasicEntanglerLayers(qml.RY, wires=range(4)) return [qml.expval(qml.PauliZ(i)) for i in range(4)] cloud based quantum machine learning software

import pennylane as qml import torch dev = qml.device("braket.aws.qubit", device_arn="arn...", wires=4) class HybridModel(torch