k-Nearest Neighbours (k-NN) is a widely used non-parametric, instance-based classification algorithm; however, its traditional implementation suffers from high computational cost, scalability limitations, and performance degradation in high-dimensional spaces. To address these challenges, this paper proposes a hybrid quantum-classical k-Nearest Neighbours (Qk-NN) model that integrates quantum feature encoding and quantum-assisted feature transformation with classical k-NN classification. The proposed approach is evaluated on the Pima Indians Diabetes Dataset using feature subsets of 5, 6, and 7 dimensions, selected due to quantum circuit scalability constraints. Quantum feature transformations are implemented through parameterized quantum circuits simulated on classical hardware in an ideal noiseless environment. Model performance is assessed using accuracy and log loss with a fixed Neighbourhood size of k = 5. Experimental results show that the Hybrid Qk-NN is superior to the classical k-NN in each feature subset. In particular, with five features, the Hybrid Qk-NN registers an accuracy of 77. 62 % while classical k-NN only achieves 71. 84 %, meanwhile, the loss values are quite lower. Quantum-assisted feature mapping capable of increasing the performance of classification is suggested by these results, and hybrid quantum-classical models are shown to be a viable bridge to the quantum machine learning world in the future.
k-Nearest Neighbours (k-NN) is a widely used non-parametric, instance-based classification algorithm; however, its traditional implementation suffers from high computational cost, scalability limitations, and performance degradation in high-dimensional spaces. To address these challenges, this paper proposes a hybrid quantum-classical k-Nearest Neighbours (Qk-NN) model that integrates quantum feature encoding and quantum-assisted feature transformation with classical k-NN classification. The proposed approach is evaluated on the Pima Indians Diabetes Dataset using feature subsets of 5, 6, and 7 dimensions, selected due to quantum circuit scalability constraints. Quantum feature transformations are implemented through parameterized quantum circuits simulated on classical hardware in an ideal noiseless environment. Model performance is assessed using accuracy and log loss with a fixed Neighbourhood size of k = 5. Experimental results show that the Hybrid Qk-NN is superior to the classical k-NN in each feature subset. In particular, with five features, the Hybrid Qk-NN registers an accuracy of 77. 62 % while classical k-NN only achieves 71. 84 %, meanwhile, the loss values are quite lower. Quantum-assisted feature mapping capable of increasing the performance of classification is suggested by these results, and hybrid quantum-classical models are shown to be a viable bridge to the quantum machine learning world in the future.
The k-nearest neighbor algorithm (k-NN) is a straightforward and highly effective classical machine learning method that categorises unidentified test instances by identifying the k-nearest Neighbors from the collection of m-training instances. It is widely acknowledged that integrating machine learning algorithms with quantum computing offers tremendous benefits in some applications involving higher dimensional datasets. This research examines the quantum k-nearest neighbor (QK-NN) algorithm executed within the PennyLane’s default. qubit framework. The paper assesses the benefits of incorporating quantum computing into machine learning problems by focusing on quantum circuits and quantum feature maps for quantum-enhanced classifiers. The PennyLane framework is employed to model the behaviour on four datasets viz. Iris, Wine, Rain, Indian Lever. The implementation results reveal that quantum-enhanced k-NN Combined feature maps exhibit superior performance on most of the Accuracy, Precision, Recall and F-1 score parameters compared to individual feature maps.
This paper presents a novel quantum K-nearest neighbors (QKNN) algorithm, which offers improved performance over the classical k-NN technique by incorporating quantum computing (QC) techniques to enhance classification accuracy, scalability, and robustness. The proposed modifications focus on optimizing quantum data encoding using Hadamard and rotation gates, ensuring more effective rendering of classical data in quantum states. In addition, the quantum feature extraction process is significantly enhanced by the use of entangled gates such as IsingXY and CNOT, which enables better feature interactions and class separability. A novel quantum distance metric, based on the swap test, is introduced to calculate similarity measures between various quantum states, offering superior accuracy and computational efficiency compared to traditional Euclidean distance metrics. We assess the achievement of the proposed QKNN algorithm on three benchmark datasets: Wisconsin Breast Cancer, Iris, and Bank Note Authentication, and have noted its superior performance relative to both classical k-NN (CKNN) and Quantum Neural Network (QNN). The proposed QKNN algorithm is found to achieve prediction accuracies of 98.25%, 100%, and 99.27% ,respectively, for the three datasets, while the customized QNN shows prediction accuracies of only 97.17%, 83.33%, and 86.18%, respectively. Furthermore, we address the challenges of quantum noise by incorporating a repetition encoding-based error mitigation strategy, which ensures the stability and resilience of the algorithm in noisy quantum environments. The results highlight the potential of the proposed QKNN as a scalable, efficient and robust quantum-enhanced machine learning algorithm, especially in high-dimensional and complex datasets, when traditional approaches frequently fail.
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.
This research explores quantum machine learning for image classification, comparing classical and quantum models. A classical convolutional neural network (CNN), based on the LeNet architecture, achieved 99.12% accuracy on MNIST, outperforming a quantum CNN, which attained 93.30%. Quantum encoders applied to Fashion-MNIST reached 85.60%, while quantum support vector machine (SVM), implemented for binary classification, obtained 71.34%. The study integrates hybrid quantum-classical models, leveraging quantum circuits for feature extraction. A quantum autoencoder-based classifier was introduced, demonstrating efficient data compression and classification without additional qubits. Feature encoding using RX gates mapped classical data into quantum states, and the impact of parameterized quantum circuit structures on classification accuracy was analyzed. Results indicate that specific ansatz structures yield superior accuracy, highlighting the significance of quantum circuit design. While classical models currently outperform quantum counterparts, quantum approaches reduce trainable parameters and computational complexity. Limitations in current quantum hardware, including noise and scalability, restrict performance. However, advancements in error mitigation, quantum gate efficiency, and scalable quantum architecture could bridge the gap with classical methods. These quantum techniques hold potential in aerospace applications, such as satellite image classification, anomaly detection in remote sensing, and real-time processing of high-dimensional image data. This study underscores the growing role of quantum circuits in machine learning, particularly in feature extraction and classification, suggesting that future quantum hardware could unlock new capabilities in end-to-end learning beyond hybrid approaches.
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 early detection and accurate classification of diabetes health indicators are crucial for effective disease management and prevention. This study aims to compare the performance of classical and quantum machine learning models in feature selection and classification on the Diabetes Health Indicators dataset. Initially, classical machine learning methods were employed to preprocess the data, including normalization and scaling, followed by feature selection using Lasso regression. Various traditional models, such as Logistic Regression, Decision Trees, Random Forests, Gradient Boosting, K-Nearest Neighbors, and Naive Bayes, were evaluated. Among these, the Logistic Regression model achieved the highest accuracy at 85%, while other models also demonstrated competitive performance with accuracies ranging from 82% to 85%. Subsequently, quantum machine learning techniques were applied using the selected features to assess their effectiveness. Quantum circuits were created using Cirq, and parameter optimization was performed through Quantum Feature Mapping and Quantum Feature Transformation. The Quantum Support Vector Machine (QSVM) model attained an accuracy of 84.33%, showing potential for matching the performance of traditional models. The results suggest that quantum machine learning models can offer comparable accuracy to classical methods in the classification of diabetes health indicators. This study highlights the potential benefits of integrating quantum techniques in complex data processing and recommends further exploration in future research to fully harness the capabilities of quantum machine learning.