Quantum-Aware Activation Functions in Hybrid Quantum Neural Networks: A Comparative Study on Bioinformatic Datasets

Yumin Dong, Shuang Wu

Abstract In order to explore the possibility of cross-fertilization between quantum computing and neural networks, and to analyse the effects of multiple weight remapping functions on the model performance, this article proposes two hybrid models based on weight remapping: the hybrid quantum neural network (HQNN-WR) and the hybrid quantum convolutional neural network (HQCNN-WR). The HQNN-WR model uses a two-layer fully connected network to process the input features, performs feature transformation by applying multiple weight remapping functions, and subsequently passes the processed features to the quantum neural network for computation, and finally outputs the classification results. The experimental results show that the model significantly improves the classification accuracy on Iris, Wine and Breast datasets and the convergence speed is faster. The HQCNN-WR model integrates convolutional layers, pooling layers, and fully connected layers, and prevents over-fitting through a dropout layer, and exhibits excellent performance in binary classification tasks on MNIST and KMNIST datasets. The model effectively mitigates the over-fitting problem on small sample datasets and enhances the robustness and generalization ability of the model while improving the digit recognition accuracy. By comparing different models, this article also demonstrates their significant effects on the performance of hybrid quantum neural networks, providing a new theoretical basis and experimental support for the optimization and application of quantum machine learning methods.

Dukuru Chiranjevi, Anisha Mishra, Ranjan Kumar Behera +1 more

S. K. Jeswal, Snehashish Chakraverty

A.H Abbas

Abstract 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.

Muhammad Kashif, S.M. Hussain Khalid, Alberto Marchisio +2 more

Hybrid Quantum Neural Networks (HQNNs), which combine parameterized quantum circuits with classical neural layers, are emerging as promising models in the noisy intermediate-scale quantum (NISQ) era. While quantum circuits are not naturally measured in floating point operations (FLOPs), most HQNNs (in NISQ era) are still trained on classical simulators where FLOPs directly dictate runtime and scalability. Hence, FLOPs represent a practical and viable metric to measure the computational complexity of HQNNs. In this work, we introduce FAQNAS, a FLOPs-aware neural architecture search (NAS) framework that formulates HQNN design as a multi-objective optimization problem balancing accuracy and FLOPs. Unlike traditional approaches, FAQNAS explicitly incorporates FLOPs into the optimization objective, enabling the discovery of architectures that achieve strong performance while minimizing computational cost. Experiments on five benchmark datasets (MNIST, Digits, Wine, Breast Cancer, and Iris) show that quantum FLOPs dominate accuracy improvements, while classical FLOPs remain largely fixed. Pareto-optimal solutions reveal that competitive accuracy can often be achieved with significantly reduced computational cost compared to FLOPs-agnostic baselines. Our results establish FLOPs-awareness as a practical criterion for HQNN design in the NISQ era and as a scalable principle for future HQNN systems.

Mohammad R. Khosravi, Maryam Zekri