Hybrid Quantum Machine Learning is an emerging field of research that combines the strengths of both classical and quantum domains, particularly when working with noisy intermediate-scale quantum (NISQ) computers. Classical models often face challenges related to the size of input data and model complexity, while quantum computing-based models are constrained by the limited availability of high-qubit, error-free quantum hardware. Hybrid models, which integrate quantum circuits with classical deep learning models, offer a solution to these limitations. Recent research has demonstrated the superiority of hybrid models over classical models in terms of performance. Motivated by these findings, in this work, 2 basic classical models Fully Connected Neural Network (FCNN), and Convolution Neural Network (CNN)-Bidirectional Long Short-Term Memory(BiLSTM) are modified using different quantum circuits and performance of these hybrid models is compared for heart disease detection task.
Hybrid Quantum Machine Learning is an emerging field of research that combines the strengths of both classical and quantum domains, particularly when working with noisy intermediate-scale quantum (NISQ) computers. Classical models often face challenges related to the size of input data and model complexity, while quantum computing-based models are constrained by the limited availability of high-qubit, error-free quantum hardware. Hybrid models, which integrate quantum circuits with classical deep learning models, offer a solution to these limitations. Recent research has demonstrated the superiority of hybrid models over classical models in terms of performance. Motivated by these findings, in this work, 2 basic classical models Fully Connected Neural Network (FCNN), and Convolution Neural Network (CNN)-Bidirectional Long Short-Term Memory(BiLSTM) are modified using different quantum circuits and performance of these hybrid models is compared for heart disease detection task.
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.
The integration of quantum computing principles with classical deep learning architectures has emerged as a promising direction for advancing image recognition systems beyond the limitations of purely classical convolutional neural networks (CNNs). This study proposes and analyzes a hybrid quantum–classical deep learning framework based on quanvolutional neural network (QNN) principles, where quantum circuits are embedded within convolutional feature extraction layers to enhance representation capability. The research investigates how quantum-enhanced filters, particularly quanvolutional layers, contribute to improved feature diversity, non-linear transformation capacity, and classification accuracy in image recognition tasks. Building upon established convolutional architectures and quantum machine learning theories, the proposed framework integrates quantum random circuit encoding with classical deep neural networks to evaluate performance gains in comparison to conventional CNN models. The study further incorporates theoretical insights from hybrid quantum-classical learning systems, emphasizing the role of parameterized quantum circuits in feature mapping and optimization stability. The methodology is grounded in NISQ-era quantum computing constraints and leverages hybrid optimization strategies to ensure computational feasibility. Experimental evaluation demonstrates that quanvolution-based models can provide improved robustness in feature extraction, particularly in high-dimensional image spaces, while maintaining competitive computational efficiency. However, scalability limitations and noise sensitivity in quantum circuits remain key challenges. Overall, this work contributes to the growing field of quantum-enhanced machine learning by providing a structured performance analysis of hybrid quanvolutional architectures and highlighting their potential role in next-generation intelligent visual recognition systems.
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 one of the major tasks in deep learning. Various deep learning methodologies like Quantum Neural Network are playing a vital role in classifying the images. In traditional neural networks neural layers are used to extract the features from the image to train the model where some of the details may be excluded. In quantum methodology parameterised circuits are used to identify complex boundaries to extract high quality features from the images. In the proposed method a hybrid QNN model is developed for binary image classification using quantum computing. Proposed model efficiently works by performing computation on Noise Intermediate-Scale Quantum (NISQ) devices. which represent the current forefront of quantum computing capabilities. This is achieved by integrating a small, two-qubit quantum circuit with a classical convolutional backbone using a tensor product state. The proposed model achieves a classification accuracy of 90.1% on binary image datasets, marking a significant improvement. Furthermore, we evaluate the generalization performance of both the H-QNN and baseline CNN models in image retrieval tasks. The results demonstrate the superior generalization capabilities of the H-QNN, especially in mitigating overfitting on small datasets, making it a promising solution for real-world applications.
The emergence of deep vision models such as convolutional neural networks and vision transformers has revolutionized computer vision, enabling significant advancements in image classification, object detection, and segmentation. In parallel, the rapid development of quantum computing has spurred interest in quantum machine learning (QML), which integrates the strengths of quantum computation with the representational power of deep learning. In QML, parameterized quantum circuits offer the potential to capture complex image features, define complex decision boundaries, and provide other computational advantages. This paper investigates hybrid quantum-classical vision architectures, with a focus on hybrid quantum-classical convolutional neural networks and hybrid quantum-classical vision transformers. These hybrid models explore both quantum pre-processing and post-processing of data, respectively, where quantum circuits are strategically integrated into the data pipeline to enhance model performance. Our results suggest that these hybrid models can enhance accuracy and computational efficiency in vision-related tasks, even with the constraints of current noisy intermediate-scale quantum devices.
Deep learning techniques have significantly enhanced medical image classification for disease diagnosis. However, traditional deep learning models face several challenges, such as dependence on large annotated datasets, complex architectural layers, and millions of trainable parameters, which can increase computational load and cause overfitting. Although quantum computing offers potential for parallel processing and improved feature representation, its practical use is limited by the current capabilities of Noisy Intermediate-Scale Quantum (NISQ) hardware. To overcome these issues, Hybrid Quantum–Classical Convolutional Neural Networks (HQCNNs) have been developed, using quantum properties like superposition and entanglement to improve feature extraction and classification of medical images. This review offers a thorough analysis of 50 peer-reviewed studies published from 2021 to 2026 and introduces a taxonomy of HQCNN architectures. It classifies existing approaches into three categories: Quantum-to-Classical, Classical-to-Quantum, and Hybrid Quantum Transfer Learning. The studies reviewed encompass a diverse range of medical imaging applications, including binary and multi-class disease classification using X-ray, CT, MRI, ultrasound, dermoscopic, retinal, and mammographic images. Additionally, the review compares HQCNNs with their classical CNN counterparts, exploring how factors like quantum circuit design, dataset size, class imbalance, task complexity, evaluation protocols, and hardware limitations influence classification performance. It concludes with an analysis of the key limitations of current HQCNN models, discusses challenges in deploying NISQ devices practically, and proposes future research directions to bridge the gap between experimental HQCNN frameworks and reliable clinical diagnostic systems.