With the rapid growth of data volume and complexity, traditional machine learning algorithms encounter significant challenges in handling large-scale and high-dimensional datasets. To address these limitations, this paper proposes an enhanced classification framework based on Quantum Resonance Dimensionality Reduction (QRDR) and Hybrid Quantum Ensemble Learning (HQEL). First, a Multi-Kernel Quantum Support Vector Machine (MKQSVM) is introduced, which integrates the advantages of multiple quantum kernel functions and employs Bayesian optimization to automatically determine the optimal kernel combination. This approach enhances the model’s adaptability and generalization ability on heterogeneous data. Second, the hybrid quantum ensemble learning model combines MKQSVM and Quantum Convolutional Neural Network (QCNN) through a stacking ensemble strategy with a classical meta-learner, thereby achieving improved prediction accuracy and stability. Moreover, the proposed QRDR algorithm is utilized to reduce the dimensionality of high-dimensional data while retaining critical features and minimizing redundancy, which effectively improves both training efficiency and predictive performance of the quantum models. Experimental results demonstrate that the proposed method outperforms traditional machine learning approaches on multiple benchmark datasets, particularly in classifying complex and high-dimensional data.
With the rapid growth of data volume and complexity, traditional machine learning algorithms encounter significant challenges in handling large-scale and high-dimensional datasets. To address these limitations, this paper proposes an enhanced classification framework based on Quantum Resonance Dimensionality Reduction (QRDR) and Hybrid Quantum Ensemble Learning (HQEL). First, a Multi-Kernel Quantum Support Vector Machine (MKQSVM) is introduced, which integrates the advantages of multiple quantum kernel functions and employs Bayesian optimization to automatically determine the optimal kernel combination. This approach enhances the model’s adaptability and generalization ability on heterogeneous data. Second, the hybrid quantum ensemble learning model combines MKQSVM and Quantum Convolutional Neural Network (QCNN) through a stacking ensemble strategy with a classical meta-learner, thereby achieving improved prediction accuracy and stability. Moreover, the proposed QRDR algorithm is utilized to reduce the dimensionality of high-dimensional data while retaining critical features and minimizing redundancy, which effectively improves both training efficiency and predictive performance of the quantum models. Experimental results demonstrate that the proposed method outperforms traditional machine learning approaches on multiple benchmark datasets, particularly in classifying complex and high-dimensional data.
Abstract Quantum Machine Learning (QML) has emerged as a promising paradigm that leverages the principles of quantum mechanics—such as superposition and entanglement—to overcome the limitations of classical machine learning in handling complex, high-dimensional data. However, practical challenges such as limited coherence times of qubits and the integration of quantum circuits with classical architectures have hindered the full realization of QML’s potential. To address these challenges, this paper proposes a novel hybrid architecture, termed the Classical-Quantum Hybrid Convolutional Neural Network (CQHCNN), which integrates quantum-inspired layers into conventional deep learning frameworks. These quantum layers utilize quantum information processing to extract complex features, while the classical components perform feature extraction and classification. This paper evaluates the performance of our model on several benchmark datasets, including MNIST, Fashion-MNIST, CIFAR-10 and COVID-19 Radiography, demonstrating its ability to outperform some other Quantum Convolutional Neural Networks (QCNNs) in terms of many metrics. Experimental results indicate that the hybrid model achieves significant improvements in classification accuracy and computational efficiency, highlighting the potential of quantum-inspired techniques to enhance classical machine learning models.
Image classification has become a fundamental task in computer vision with applications in areas such as medical imaging, agriculture, environmental monitoring, and automated surveillance. Traditional machine learning techniques have achieved reasonable performance in classification tasks; however, they often struggle when dealing with high-dimensional and complex image datasets. Deep learning models, particularly Convolutional Neural Networks (CNNs), have significantly improved image classification performance by automatically learning hierarchical feature representations. Despite these advancements, classical deep learning models may still face challenges related to computational complexity and large-scale data processing.In recent years, quantum machine learning has emerged as a promising paradigm that combines principles of quantum computing with classical machine learning techniques to enhance computational efficiency and model performance. This study proposes a hybrid quantum–classical framework for image classification that integrates a deep residual network (ResNet-50) with a Quantum Support Vector Machine (QSVM). The ResNet-50 model is employed as a feature extraction mechanism to capture high-level visual representations from image data. The extracted features are then reduced in dimensionality using Principal Component Analysis (PCA) to simplify the feature space and improve computational efficiency.The reduced feature vectors are subsequently classified using a QSVM model that utilizes quantum feature maps to encode classical data into quantum states. Various quantum feature maps are explored to evaluate their impact on classification performance. Experimental results demonstrate that the hybrid quantum–classical approach achieves higher classification accuracy compared to conventional machine learning models such as Support Vector Machines and Random Forest classifiers. The proposed framework highlights the potential of combining classical deep learning architectures with quantum machine learning algorithms to address complex image classification challenges. This hybrid approach provides an efficient and scalable solution for advanced image analysis tasks and demonstrates the growing potential of quantum computing in artificial intelligence applications.
Quantum computing is a promising candidate for accelerating machine learning tasks. Limited by the control accuracy of current quantum hardware, reducing the consumption of quantum resources is the key to achieving quantum advantage. Here, we propose a quantum resonant dimensionality reduction (QRDR) algorithm based on the quantum resonant transition to reduce the dimension of input data and accelerate the quantum machine learning algorithms. After QRDR, the dimension of input data N can be reduced into the desired scale R , and the effective information of the original data will be preserved correspondingly, which will reduce the computational complexity of subsequent quantum machine learning algorithms or quantum storage. QRDR operates with polylogarithmic time complexity and reduces the error dependency from the order of 1 / ε 3 to the order of 1 / ε , compared to existing algorithms. Meanwhile, by avoiding quantum phase estimation, the consumed qubits in QRDR is independent on error ε . Therefore, compared with existing algorithms, our algorithm has achieved the optimal performance in terms of time complexity and space complexity. We demonstrate the performance of our algorithm combining with two types of quantum classifiers, quantum support vector machines and quantum convolutional neural networks, for classifying underwater detection targets and quantum many-body phase, respectively. The simulation results indicate that reduced data extremely improved the processing efficiency following the application of QRDR. As quantum machine learning continues to advance, our algorithm has the potential to be utilized in a variety of computing fields.
Abstract Quantum computing as a new computing model with parallel computing capability and high information carrying capacity, has attracted a lot of attention from researchers. Ensemble learning is an effective strategy often used in machine learning to improve the performance of weak classifiers. Currently, the classification performance of quantum classifiers is not satisfactory enough due to factors such as the depth of quantum circuit, quantum noise, and quantum coding method, etc. For this reason, this paper combined the ensemble learning idea and quantum classifiers to design a novel hybrid quantum machine learning model. Firstly, we run the Stacking method in classical machine learning to realize the dimensionality reduction of high-latitude data while ensuring the validity of data features. Secondly, we used the Bagging method and Bayesian hyperparameter optimization method applied to quantum support vector machine (QSVM), quantum K nearest neighbors (QKNN), variational quantum classifier (VQC). Thirdly, the voting method is used to ensemble the predict results of QSVM, QKNN, VQC as the final result. We applied the hybrid quantum ensemble machine learning model to malicious code detection. The experimental results show that the classification precision (accuracy, F1-score) of this model has been improved to 98.9% (94.5%, 94.24%). Combined with the acceleration of quantum computing and the higher precision rate, it can effectively deal with the growing trend of malicious codes, which is of great significance to cyberspace security.
Noisy data is inherent in quantum computing and significantly affects the performance of many quantum machine learning algorithms. Conventional Quantum Support Vector Machines (QSVMs) often lack built-in mechanisms to handle noise, which can lead to unreliable decision boundaries under noisy conditions. To address this challenge, this work proposes a Hybrid Ensemble Anti-Noise QSVM framework designed for robust classification. The proposed approach integrates neural network–based feature extraction to generate noise-resilient representations with an Anti-Noise QSVM that incorporates a sample-weighted hinge loss to reduce the influence of noisy or mislabeled samples. Furthermore, an ensemble voting mechanism combines the Anti-Noise QSVM with multiple classical classifiers to enhance stability and generalization. The framework is structured to support evaluation on synthetic and real-world datasets, including image, biomedical, and network intrusion data, under various noise conditions such as Gaussian, impulse, and label noise using quantum simulators. This hybrid quantum–classical design aims to improve robustness, scalability, and adaptability compared to conventional QSVM-based approaches.
Quantum machine learning has emerged as a promising frontier, leveraging the principles of quantum computing to enhance classical algorithms. This paper proposes an optimized Quantum Support Vector Classifier (QSVC) for the classification of the MNIST dataset (a benchmark in handwritten digit recognition). We employ a Chebyshev Parameterized Quantum Circuit (PQC) with an increased number of qubits and layers, and utilize an existing projected quantum kernel method to effectively capture the complex patterns in the data. To enhance model robustness, data augmentation techniques are applied, and dimensionality reduction is performed using Principal Component Analysis (PCA). The proposed QSVC demonstrates competitive accuracy on both the training and testing datasets, highlighting the potential of quantum kernel methods in practical machine learning applications. A comparative analysis with classical Support Vector Machines (SVM) demonstrates the feasibility of quantum methods for high-dimensional classification tasks. Additionally, we investigate the impact of varying the number of qubits in the quantum circuit, providing in-depth insights into the scalability and performance trade-offs of quantum kernel methods.