With the limitations of current noisy intermediate scale quantum (NISQ) devices, there is a need for hybrid quantum-classical learning models to operate more efficiently within existing hardware constraints. The design of cost-effective hybrid quantum-classical learning systems is therefore imperative for various applications, including biosignal processing in the healthcare sector. This motivates the investigation into the reduction of computational complexity of a hybrid quantumclassical neural network. We propose a new hybrid model combining a quantum neural network (QNN) with a binary neural network (BNN), known as quBNN, with the aim to reduce model complexity. For benchmarking, we consider a classical multilayer perceptron (MLP) and a standard nonbinarised version of quBNN that combines a QNN with an MLP, named as quilp. We evaluated all models on a multi-class dataset containing multimodal biosignals. We find that quMLP can outperform the classical MLP with minimal additional complexity, while quBNN maintains performance comparable with the classical MLP, with reduced computational complexity. We apply pruning and quantisation techniques to the QNN within the proposed quBNN model, aiming to further reduce its complexity. This compression yields substantial reductions in parameter and quantum gate count when compiled for quantum hardware, albeit with performance degradation.
With the limitations of current noisy intermediate scale quantum (NISQ) devices, there is a need for hybrid quantum-classical learning models to operate more efficiently within existing hardware constraints. The design of cost-effective hybrid quantum-classical learning systems is therefore imperative for various applications, including biosignal processing in the healthcare sector. This motivates the investigation into the reduction of computational complexity of a hybrid quantumclassical neural network. We propose a new hybrid model combining a quantum neural network (QNN) with a binary neural network (BNN), known as quBNN, with the aim to reduce model complexity. For benchmarking, we consider a classical multilayer perceptron (MLP) and a standard nonbinarised version of quBNN that combines a QNN with an MLP, named as quilp. We evaluated all models on a multi-class dataset containing multimodal biosignals. We find that quMLP can outperform the classical MLP with minimal additional complexity, while quBNN maintains performance comparable with the classical MLP, with reduced computational complexity. We apply pruning and quantisation techniques to the QNN within the proposed quBNN model, aiming to further reduce its complexity. This compression yields substantial reductions in parameter and quantum gate count when compiled for quantum hardware, albeit with performance degradation.
Image classification is an important application for deep learning. With the advent of quantum technology, quantum neural networks (QNNs) have become the focus of research. Traditional deep learning-based image classification involves using a convolutional neural network (CNN) to extract features from the image and a multi-layer perceptron (MLP) network to create the decision boundaries. However, quantum circuits with parameters can extract rich features from images and also create complex decision boundaries. This paper proposes a hybrid QNN (H-QNN) model designed for binary image classification that capitalizes on the strengths of quantum computing and classical neural networks. Our H-QNN model uses a compact, two-qubit quantum circuit integrated with a classical convolutional architecture, making it highly efficient for computation on noisy intermediate-scale quantum (NISQ) devices that are currently leading the way in practical quantum computing applications. Our H-QNN model significantly enhances classification accuracy, achieving a 90.1% accuracy rate on binary image datasets. In addition, we have extensively evaluated baseline CNN and our proposed H-QNN models for image retrieval tasks. The obtained quantitative results exhibit the generalization of our H-QNN for downstream image retrieval tasks. Furthermore, our model addresses the issue of overfitting for small datasets, making it a valuable tool for practical applications.
Quantum machine learning has emerged as a promising approach to enhance computational efficiency by leveraging the principles of quantum computing. However, the limitations of current Noisy Intermediate-Scale Quantum (NISQ) devices, such as noise, limited qubit availability, and circuit depth constraints, restrict the implementation of fully quantum models. To address these challenges, this study focuses on the design, implementation, and performance evaluation of hybrid quantum-classical machine learning (HQML) models. The proposed approach integrates parameterized quantum circuits with classical optimization techniques to enable efficient learning within NISQ environments. The study employs standard benchmark datasets, including Iris, Breast Cancer, and MNIST, to evaluate the performance of the hybrid model. The results indicate that the HQML model achieves competitive accuracy on small and medium-sized datasets while maintaining balanced precision, recall, and F1-score. However, performance declines for complex datasets due to hardware limitations and noise effects. Additionally, the hybrid model demonstrates a lower number of parameters compared to classical deep learning models but requires higher training time due to iterative quantum-classical optimization. The findings highlight that hybrid quantum-classical models provide a practical and scalable approach for utilizing quantum computing in the current technological landscape. Although challenges related to noise, scalability, and computational overhead persist, advancements in quantum hardware and algorithm design are expected to improve performance. This study contributes to the growing field of quantum machine learning by providing a systematic framework for evaluating hybrid models on NISQ devices and identifying key areas for future research.
Protein-ligand binding affinity is critical in drug discovery, but experimentally determining it is time-consuming and expensive. Artificial intelligence (AI) has been used to predict binding affinity, significantly accelerating this process. However, the high-performance requirements and vast datasets involved in affinity prediction demand increasingly large AI models, requiring substantial computational resources and training time. Quantum machine learning has emerged as a promising solution to these challenges. In particular, hybrid quantum-classical models can reduce the number of parameters while maintaining or improving performance compared to classical counterparts. Despite these advantages, challenges persist: why hybrid quantum models achieve these benefits, whether quantum neural networks (QNNs) can replace classical neural networks, and whether such models are feasible on noisy intermediate-scale quantum (NISQ) devices. This study addresses these challenges by proposing a hybrid quantum neural network (HQNN) that empirically demonstrates the capability to approximate non-linear functions in the latent feature space derived from classical embedding. The primary goal of this study is to achieve a parameter-efficient model in binding affinity prediction while ensuring feasibility on NISQ devices. Numerical results indicate that HQNN achieves comparable or superior performance and parameter efficiency compared to classical neural networks, underscoring its potential as a viable replacement. This study highlights the potential of hybrid QML in computational drug discovery, offering insights into its applicability and advantages in addressing the computational challenges of protein-ligand binding affinity prediction.
Quantum Machine Learning represents a promising approach to address complex computational challenges beyond classical computing capabilities. In this study, we investigate its application to healthcare, focusing specifically on the classification of cardiovascular diseases. Classical Machine Learning methods often struggle with high-dimensional data, resulting in limited accuracy and reduced generalization, especially in real-world medical scenarios. To overcome these issues, we propose a Hybrid Quantum-Neural Network that combines classical Autoencoders for dimensionality reduction with Quantum Neural Networks, facilitating effective data processing compatible with current NISQ hardware. Evaluated on the Cleveland dataset, our HQNN achieves state-of-the-art classification accuracy up to 90.98%, outperforming purely classical counterparts by capturing complex feature relationships in the quantum domain. We further demonstrate the effectiveness of the proposed HQNN model in scenarios characterized by limited training data and realistic noisy quantum environments, performing robustness analyses and simulations using IBM’s quantum hardware framework. These findings highlight the practical advantages of our Hybrid Quantum-Neural Network, underscoring its suitability and immediate applicability for real-world healthcare scenarios. The code is made publicly available.
Hybrid quantum-classical machine learning (HQML) models represent an emerging computational paradigm that combines the strengths of quantum computing and classical artificial intelligence to solve complex learning problems more efficiently.Quantum computing offers massive parallelism, superposition, and entanglement, enabling computational capabilities beyond classical systems for selected tasks.However, current quantum hardware remains limited by noise, decoherence, and scalability constraints.Hybrid architectures overcome these limitations by integrating quantum processors with classical machine learning frameworks, allowing practical implementation on Noisy Intermediate-Scale Quantum (NISQ) devices.This review paper presents a comprehensive study of hybrid quantum-classical machine learning models, including their architecture, working principles, quantum circuits, optimization techniques, and applications in healthcare, cybersecurity, finance, robotics, and big data analytics.The paper also discusses quantum neural networks, variational quantum circuits, quantum support vector machines, and quantum generative models.Furthermore, comparative analysis between classical and hybrid quantum systems is presented to evaluate performance, scalability, computational efficiency, and implementation challenges.Major issues such as quantum noise, hardware limitations, algorithm complexity, and high operational cost are examined in detail.Finally, future research directions and emerging trends in hybrid quantum-classical computing are discussed.The review concludes that HQML models have the potential to revolutionize next-generation intelligent computing systems.
Image classification is a fundamental task in deep learning, and recent advances in quantum computing have generated significant interest in quantum neural networks. Traditionally, Convolutional Neural Networks (CNNs) are employed to extract image features, while Multilayer Perceptrons (MLPs) handle decision making. However, parameterized quantum circuits offer the potential to capture complex image features and define sophisticated decision boundaries. In this paper, we present a novel Hybrid Quantum–Classical Neural Network (H-QNN) for image classification, and demonstrate its effectiveness using the MNIST dataset. Our model combines quantum computing with classical supervised learning to enhance classification accuracy and computational efficiency. In this study, we detail the architecture of the H-QNN, emphasizing its capability in feature learning and image classification. Experimental results demonstrate that the proposed H-QNN model outperforms conventional deep learning methods in various training scenarios, showcasing its effectiveness in high-dimensional image classification tasks. Additionally, we explore the broader applicability of hybrid quantum–classical approaches in other domains. Our findings contribute to the growing body of work in quantum machine learning, and underscore the potential of quantum-enhanced models for image recognition and classification.