Quantum-Enhanced Multi-Head Attention for Hybrid Classical-Quantum Neural Networks

The function relies on Z-expectation values of qubits, simulated using Qiskit Aer's statevector simulator. These computations are encapsulated in the QuantumMultiHeadAttentionLayer, which integrates seamlessly with PyTorch's module structure. The core model, HybridAttentionCIFAR, consists of a combination of convolutional layers, a linear embedding layer, the quantum attention layer, and a fully connected output layer.

The convolutional layers extract features from input images, which are then transformed into embeddings compatible with the quantum attention layer. The quantum attention mechanism processes these embeddings, enhancing feature representation, and the final fully connected layer maps the processed embeddings to class logits for classification. The get_cifar_subset function prepares the CIFAR-10 data, creating small subsets for training and validation.

These subsets are batched using PyTorch's DataLoader. The train_cifar_qattn function orchestrates the training process, configuring the model, optimizer, and loss function. Over multiple epochs, it computes training and validation losses and accuracies, updating model parameters using the Adam optimizer.