The advent of Noisy Intermediate-Scale Quantum (NISQ) machines has brought new opportunities for expanding machine learning abilities with the help of quantum computing. Herein, a complete hybrid quantum-classical learning system is introduced that incorporates classical preprocessing, quantum feature extraction, and classical machine learning in a strategic way to surpass the performance of existing systems for the tasks of complex pattern recognition. We perform large-scale experiments with two popular benchmark datasets: the Iris flower dataset and the Wisconsin Breast Cancer Diagnostic dataset. Our framework shows consistently improving accuracy of$2-7 \%$over stark classical methods without sacrificing practicality on state-of-the-art NISQ hardware. With rigorous ablation studies and cross-validation with cutting-edge approaches, we conclude that the best task distribution between traditional and quantum processors is key to near-term quantum advantage. Our introduced framework effectively achieves a quantum circuit depth reduction of 40 % using smart classical preprocessing along with improving classification performance using quantum-augmented feature representation. Our implementation offers both hardware-based execution with Qiskit as well as fast classical simulation modes, so it is available for researchers with limited quantum computing capabilities.
The advent of Noisy Intermediate-Scale Quantum (NISQ) machines has brought new opportunities for expanding machine learning abilities with the help of quantum computing. Herein, a complete hybrid quantum-classical learning system is introduced that incorporates classical preprocessing, quantum feature extraction, and classical machine learning in a strategic way to surpass the performance of existing systems for the tasks of complex pattern recognition. We perform large-scale experiments with two popular benchmark datasets: the Iris flower dataset and the Wisconsin Breast Cancer Diagnostic dataset. Our framework shows consistently improving accuracy of2−7%over stark classical methods without sacrificing practicality on state-of-the-art NISQ hardware. With rigorous ablation studies and cross-validation with cutting-edge approaches, we conclude that the best task distribution between traditional and quantum processors is key to near-term quantum advantage. Our introduced framework effectively achieves a quantum circuit depth reduction of 40 % using smart classical preprocessing along with improving classification performance using quantum-augmented feature representation. Our implementation offers both hardware-based execution with Qiskit as well as fast classical simulation modes, so it is available for researchers with limited quantum computing capabilities.
Hybrid quantum-classical computing offers a promising pathway to harness quantum advantages while mitigating the hardware limitations of noisy intermediate-scale quantum (NISQ) devices. This work proposes a modular hybrid architecture that combines classical preprocessing, quantum-enhanced feature mapping through parameterized quantum circuits (PQCs), and adaptive classical optimization. The framework is designed to improve robustness and generalization of machine learning models in noisy environments, addressing critical issues such as overfitting, noise sensitivity, and hardware-induced errors. Experimental evaluations were conducted on benchmark datasets, including Iris, reduced-dimension MNIST, synthetic noisy XOR, Wine Quality, and Breast Cancer Wisconsin. The proposed model was tested under multiple noise models, such as Gaussian, salt-and-pepper, Poisson, and quantum-specific errors (bit-flip, phase-flip, and depolarizing), using 5-fold cross-validation and repeated trials for statistical rigor. Key performance metrics included Classification Accuracy (CA), Robustness Index (RI), and Training Stability (TS), with results reported as mean ± standard deviation and supported by paired t-tests (p < 0.05). Results demonstrate that the hybrid model consistently outperforms classical support vector machines (SVM) and standalone variational quantum classifiers (VQC) in noisy conditions, achieving smaller accuracy degradation and higher stability. The combination of quantum- enhanced feature mapping, optimizer synergy, and modular design is shown to be central to its resilience.
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.
Quantum computing has emerged as one of the most influential paradigms capable of transforming computational processes that struggle under classical limitations. In recent years, hybrid quantum–classical algorithms have gained significant attention for their ability to leverage the strengths of both architectures, especially within the Noisy Intermediate-Scale Quantum (NISQ) era. This research paper explores a quantum-enhanced optimization framework designed to improve machine learning model performance through parameterized quantum circuits (PQCs). The goal of this study is to provide a practical, scalable approach that can be integrated into existing machine learning workflows without requiring fully developed fault-tolerant quantum hardware. The proposed framework uses classical preprocessing to encode data into quantum states, followed by quantum transformations that explore richer feature spaces due to quantum superposition and entanglement. A classical optimizer then updates the quantum circuit parameters based on measurement results, forming an end-to-end feedback loop. This hybrid model aims to demonstrate improved optimization efficiency, reduction in loss-function plateaus, and better generalization for specific ML tasks involving small to medium-sized datasets. This paper provides a comprehensive investigation of methodology, literature background, and experimental reasoning with statistical analysis, along with conceptual diagrams illustrating system architecture and circuit layouts. The outcomes further strengthen the viability of hybrid quantum–classical computation as a promising direction for advanced machine learning systems.
Early diagnosis of breast cancer is crucial to combat its severity. Yet, it’s a challenging endeavor to tackle via traditional diagnosis methods. Several machine learning architectures have already been proposed to assist medical experts in diagnosis. However, architectural complexity hinders the goal of developing a cost-effective diagnosis model. Recently, quantum computing has become popular in machine learning due to its potential computational benefits. Although quantum computing has yet to show any significant improvement in machine learning due to its limited resources and complexity, it may reduce the computational complexity of machine learning in the future. Therefore, we proposed a novel hybrid classical-quantum machine learning model for breast cancer prediction. We collected the BreastMNIST dataset of 780 benign and malignant full-image mammogram samples. Our task is to create an efficient breast cancer-classifying hybrid machine learning model. The proposed method employs a modified ResNet50 as a feature extractor and a Quantum Neural Network to train on that feature and generate desired predictions. A Convolutional layer is applied as a dimension-reduction technique for features generated through ResNet50. Our architecture proved superior to other state-of-the-art hybrid methods, achieving an accuracy of 69% against 67%. An in-depth comparative analysis has been performed that shows the novelty of our research. This study paves the path for hybrid classical-quantum machine learning research in medical image classification.
Breast cancer is a major health concern, and early detection can make a great difference in treatment and survival rates for breast cancer patients. Machine learning methods have noticeably improved prediction accuracy on high-dimensional medical datasets and have become widely used tools in medical diagnostics. The transition to a quantum computational framework has opened new directions in machine learning research. By using qubits instead of classical bits, quantum models may provide new ways to represent and process complex medical data. In medical diagnostics, this has led to a growing interest in whether quantum approaches can improve classification performance more effectively than classical methods. The purpose of this paper is to compare classical and quantum machine learning models on the task of breast cancer classification and to determine whether quantum models can achieve higher accuracy and faster prediction on a selected dataset. Alongside the main simulator-based experiment, a smaller experiment was performed on a real IBM quantum computer to demonstrate the practical execution of the same task under current hardware constraints. In the practical part of the study, the Wisconsin Diagnostic Breast Cancer (WDBC) dataset was used. This dataset consists of 569 samples with 30 numerical features extracted from digitized fine needle aspirate images and provides a reliable basis for evaluating model performance. A comparative analysis was conducted between classical and quantum machine learning models, including a support vector machine (SVM), an artificial neural network (ANN), a quantum support vector machine (QSVM), and a hybrid quantum-classical neural network (QNN). In the simulator-based experiment, both SVM and ANN achieved an accuracy of 0.956 with an F1-score of 0.965 on the test set, while QSVM reached an accuracy of 0.807 with an F1-score of 0.866 and the Hybrid QNN achieved 0.623 accuracy with an F1-score of 0.677. These quantum and hybrid models also required substantially longer training times than the classical baselines. A small hardware experiment on an IBM quantum device further illustrates both the practical feasibility and the current limitations of executing this classification task on noisy intermediate-scale quantum hardware.
Quantum Machine Learning (QML) combines principles of quantum computing with tradicional Machine Learning (ML) to explore computational advantages in data processing and model efficiency. With the rise of Noisy Intermediate-Scale Quantum (NISQ) devices, hybrid quantum-classical approaches are gaining momentum, especially in domains requiring high precision such as healthcare. In this work, we investigate whether hybrid quantum computing can enhance certain aspects of classical machine learning, specifically in dataset balancing and the complexity of the neural network involved in training. To this end, we use the Indian Liver Patient Dataset as a case study to determine the presence of liver disease. We present the methodology for developing ‘QML-Liver,’ a hybrid approach that seamlessly integrates classical and quantum machine learning techniques. This includes data preprocessing, model design, and optimal configuration. Our results demonstrate that ‘QML-Liver’ improves key performance metrics, such as accuracy and precision. Additionally, we successfully reduce the number of required qubits to just one, making practical deployment more feasible. These findings underscore the potential of Quantum Machine Learning for medical diagnostics, particularly in the NISQ era.