TunnElQNN: A Hybrid Quantum-classical Neural Network for Efficient Learning
This work addresses the challenge of enhancing expressiveness and robustness in hybrid quantum-classical machine learning, though it appears incremental as it builds on existing hybrid architectures with a novel activation function.
The authors tackled the problem of improving hybrid quantum-classical neural networks by proposing TunnElQNN, a non-sequential architecture with a physics-inspired activation function, which outperformed a baseline model on a synthetic multi-class classification dataset.
Hybrid quantum-classical neural networks (HQCNNs) represent a promising frontier in machine learning, leveraging the complementary strengths of both models. In this work, we propose the development of TunnElQNN, a non-sequential architecture composed of alternating classical and quantum layers. Within the classical component, we employ the Tunnelling Diode Activation Function (TDAF), inspired by the I-V characteristics of quantum tunnelling. We evaluate the performance of this hybrid model on a synthetic dataset of interleaving half-circle for multi-class classification tasks with varying degrees of class overlap. The model is compared against a baseline hybrid architecture that uses the conventional ReLU activation function (ReLUQNN). Our results show that the TunnElQNN model consistently outperforms the ReLUQNN counterpart. Furthermore, we analyse the decision boundaries generated by TunnElQNN under different levels of class overlap and compare them to those produced by a neural network implementing TDAF within a fully classical architecture. These findings highlight the potential of integrating physics-inspired activation functions with quantum components to enhance the expressiveness and robustness of hybrid quantum-classical machine learning architectures.