Abstract The practical application of quantum machine learning algorithms, such as Quantum Neural Networks (QNNs), is currently constrained by their dependence on nascent, error-prone, and resource-intensive quantum hardware. This research introduces and validates a novel hybrid quantum- classical (HQC) algorithm engineered to deliver robust classification performance on modest, commodity classical hardware, thereby circumventing the quantum hardware bottleneck. Our multi-stage architecture strategically integrates advanced classical statistical analysis (Mahalanobis distance), a quantum-inspired kernel method (a hardened Quantum Support Vector Machine), and a quantum-inspired anomaly detection model (a Quantum Autoencoder). This ensemble was rigorously benchmarked on a synthetic dataset specifically engineered for extreme classification dinjculty (85-90%), characterized by high dimensionality, low informative features, and complex, multi- cluster class structures. The HQC algorithm achieved a notable weighted average accuracy of 42.66% across multiple experimental runs. This result is particularly significant when contextualized: it was achieved on a legacy classical server (Dell PowerEdge T110 II), yet it competitively approaches the performance envelope of state-of-the-art QNNs (55-70%) that require genuine quantum processors to exploit phenomena like superposition and entanglement. This work presents a compelling case for the strategic value of hybrid models as a pragmatic, resource- enjcient, and immediately deployable solution. It effectively bridges the gap between the theoretical promise of quantum computing and the practical demands of current computational ecosystems, offering a viable and powerful pathway toward the democratization of complex problem- solving
Abstract The practical application of quantum machine learning algorithms, such as Quantum Neural Networks (QNNs), is currently constrained by their dependence on nascent, error-prone, and resource-intensive quantum hardware. This research introduces and validates a novel hybrid quantum- classical (HQC) algorithm engineered to deliver robust classification performance on modest, commodity classical hardware, thereby circumventing the quantum hardware bottleneck. Our multi-stage architecture strategically integrates advanced classical statistical analysis (Mahalanobis distance), a quantum-inspired kernel method (a hardened Quantum Support Vector Machine), and a quantum-inspired anomaly detection model (a Quantum Autoencoder). This ensemble was rigorously benchmarked on a synthetic dataset specifically engineered for extreme classification dinjculty (85-90%), characterized by high dimensionality, low informative features, and complex, multi- cluster class structures. The HQC algorithm achieved a notable weighted average accuracy of 42.66% across multiple experimental runs. This result is particularly significant when contextualized: it was achieved on a legacy classical server (Dell PowerEdge T110 II), yet it competitively approaches the performance envelope of state-of-the-art QNNs (55-70%) that require genuine quantum processors to exploit phenomena like superposition and entanglement. This work presents a compelling case for the strategic value of hybrid models as a pragmatic, resource- enjcient, and immediately deployable solution. It effectively bridges the gap between the theoretical promise of quantum computing and the practical demands of current computational ecosystems, offering a viable and powerful pathway toward the democratization of complex problem- solving
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.
Abstract Quantum Neural Networks (QNNs) present a promising research direction in image recognition, demonstrating significant potential for various applications. However, QNNs with a fixed circuit topology structure are prone to encountering the barren plateaus during training when processing exponential number of qubits, which limit their performance and scalability. In this paper, a novel Hybrid Quantum-Classical Inception-inspired Convolutional Neural Networks (HQCNN) is proposed. The HQCNN enhances feature learning capability and mitigates the barren plateau issue by integrating quantum convolutional kernels with diverse circuit topologies across multiple channels. The architecture employs an amplitude encoding layer to map multi-channel classical data into quantum states, followed by diverse quantum convolutional kernels that extract feature representations. Furthermore, Hadamard gates are used to intergrate multi-channel features and incorporate quantum pooling to reduce feature dimensionality, with a fully connected layer mapping the quantum-enhancing features into class labels for classification tasks. Finally, by analyzing the influence of the circuit structures and quantum gate combinations on the models' performance, the optimal HQCNN architecture is constructed. Experimental results demonstrate that HQCNN achieves 98.5% accuracy on the MNIST-4 classification task and 92.5% accuracy on the MNIST-10 classification task, which indicates that the proposed model outperforms state-of-the-art HQCNN models and classical CNN in image classification with about 10% parameters. This study provides an effective approach for alleviating the barren plateau issue and designing multi-channel quantum convolutional neural networks for processing big classic data with fewer quantum resources.
Clustering remains a cornerstone of unsupervised learning, yet classical algorithms often struggle with the inherent complexities of modern datasets, such as high dimensionality, non-linear separability, and significant noise. This paper introduces the Hybrid Quantum-Classical Clustering Algorithm (HQCA), a novel framework that leverages the power of quantum computation and the robustness of classical machine learning to overcome these limitations. HQCA integrates a quantum kernel estimation phase, utilizing a parameterized quantum circuit to project data into a high-dimensional Hilbert space, thereby enhancing cluster separability where classical kernels fail. Furthermore, it employs a quantum-enhanced optimization routine, based on the Quantum Approximate Optimization Algorithm (QAOA), to determine optimal cluster centroids, thereby mitigating the common pitfall of local minima encountered by algorithms like K-Means. The algorithm also incorporates a quantum-inspired gap statistic to autonomously determine the optimal number of clusters. We rigorously evaluate HQCA against state-of-the-art classical clustering methods, including K-Means, DBSCAN, and Spectral Clustering, on multiple real-world benchmark datasets from the UCI repository, such as Iris, Wine, and a high-dimensional image dataset (Fashion-MNIST). Our results demonstrate that HQCA consistently achieves superior performance across multiple evaluation metrics, including Purity, Silhouette Score, and Adjusted Rand Index (ARI), particularly excelling in scenarios involving complex, non-linear data manifolds. The proposed hybrid paradigm not only signifies a substantial leap in clustering accuracy and robustness but also provides a scalable pathway for leveraging current Noisy Intermediate-Scale Quantum (NISQ) devices in practical data analytics workflows, marking a critical step toward potential quantum advantage in unsupervised learning. We emphasize that the demonstrated advantage is algorithmic and representational, not an asymptotic runtime advantage.
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.
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.
Quantum computers acting as ML accelerators have attracted considerable interest as a promising avenue for addressing the limitations of classical computing confronted with solving complex, high-dimensional problems and handling large data sets. The most exciting aspect of quantum computing lies in its quantum advantage via techniques such as quantum parallelism, superposition, and entanglement which allows not only for rapid data processing, but also a means to resolve complex optimization problems beyond the capacity of classical algorithms. Nevertheless, the usage of quantum computing in machine learning is still a fledgling domain, with challenges concerning quantum noise, hardware restrictions, and algorithmic development. In particular, this research is concerned with the development of and study on hybrid quantum-classical algorithms that target to improve ML models in terms of computational capabilities by making use of the best of both worlds. To address this, researchers are developing hybrid algorithms that leverage the strengths of quantum computing for specific tasks, like optimization and pattern recognition, while still relying on classical computing for more flexible and larger-scale processing, ultimately achieving more efficient machine learning systems that can handle large datasets and complex problem-solving more effectively. It includes potential hybrid approaches like quantum-enhanced optimization algorithms, quantum neural networks, and quantum support vector machines, and examines how such algorithms can be used for practical ML problems including data classification, regression and clustering. Furthermore, the paper proposes that hybrid quantum algorithms offer several advantages over their classical counterparts, such as faster convergence rates, higher accuracy in high-dimensional spaces, and improved handling of non-linear and complex problem landscapes. Key advancements in the area of data processing speed and efficiency in problem-solving with and without quantum in the machine learning workflow are also highlighted in the research through comparative analysis and simulation studies. Lastly, the paper concludes by outlining the obstacles yet to be surmounted and the opportunities that lie ahead for hybrid quantum-classical algorithms, such as further improvement of quantum hardware and error mitigation methods, as well as the design of new algorithmic paradigms that seamlessly integrates the classical and quantum computing spaces.