Data clustering consists of grouping data without prior training, a process known as unsupervised learning. Given the constant growth of database volumes, contemporary computers require significant computational time to perform this partitioning. To effectively address this challenge, quantum computing offers a promising perspective, as it could eventually provide more efficient resources for clustering massive datasets. However, current quantum computers are not yet mature; they are prone to noise, which limits their practical utility. In this context, we have designed a hybrid classical-quantum clustering algorithm tailored for the current Noisy Intermediate-Scale Quantum (NISQ) era. We evaluated this algorithm under various conditions and across different types of datasets, comparing it against several well-known clustering algorithms. Our results demonstrate that, under certain conditions, quantum noise can actually provide an advantage to our algorithm. Furthermore, we worked on deploying this algorithm on actual quantum processors and developed tools to optimize this deployment. Finally, to diversify the quantum architectures used, we extended our research to include neutral-atom quantum computers.
Data clustering consists of grouping data without prior training, a process known as unsupervised learning. Given the constant growth of database volumes, contemporary computers require significant computational time to perform this partitioning. To effectively address this challenge, quantum computing offers a promising perspective, as it could eventually provide more efficient resources for clustering massive datasets. However, current quantum computers are not yet mature; they are prone to noise, which limits their practical utility. In this context, we have designed a hybrid classical-quantum clustering algorithm tailored for the current Noisy Intermediate-Scale Quantum (NISQ) era. We evaluated this algorithm under various conditions and across different types of datasets, comparing it against several well-known clustering algorithms. Our results demonstrate that, under certain conditions, quantum noise can actually provide an advantage to our algorithm. Furthermore, we worked on deploying this algorithm on actual quantum processors and developed tools to optimize this deployment. Finally, to diversify the quantum architectures used, we extended our research to include neutral-atom quantum computers.
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.
Quantum computing exploits superposition, entanglement, and interference to address problems beyond classical reach, yet current devices are limited by noise and scale. Hybrid quantum–classical approaches therefore play a key role in achieving near-term utility by integrating quantum subroutines into classical workflows. Within this setting, Quantum Artificial Intelligence (QAI) offers new opportunities for machine learning. This thesis focuses on unsupervised Quantum Machine Learning, motivated by the abundance of unlabeled data and the importance of tasks such as clustering, dimensionality reduction, and anomaly detection. We study how to design hardware-efficient quantum subroutines, identify unsupervised paradigms well suited to hybrid computation, and assess potential advantages over classical methods. The contributions include a critical review of existing QAI approaches, the development of shallow-depth quantum subroutines for core operations, and the proposal of hybrid algorithms evaluated on synthetic and real datasets. The results highlight when quantum components provide practical value and clarify trade-offs between performance and complexity under current hardware constraints.
Quantum Machine Learning (QML) has emerged as a promising paradigm that combines principles of quantum computation with classical learning techniques to address challenges posed by complex, high-dimensional, and non-linear datasets. This work presents a systematic experimental study of quantum-enhanced classification and clustering using hybrid quantum–classical models evaluated on synthetically generated datasets. Quantum Support Vector Machines (QSVM) and Variational Quantum Classifiers (VQC) are implemented using the Qiskit framework and benchmarked against established classical algorithms. The synthetic datasets are carefully designed to control linearity, dimensionality, class overlap, and nonlinear structure, enabling a detailed performance comparison under both ideal and noisy simulation conditions. The experimental results indicate that quantum feature representations can enhance class separability in non-linear scenarios, while also highlighting the practical limitations imposed by noise sensitivity and circuit depth constraints inherent to Noisy Intermediate-Scale Quantum (NISQ) devices .
Quantum computing has emerged as a promising tool for transforming the landscape of computing technology. Recent efforts have applied quantum techniques to classical database challenges, such as query optimization, data integration, index selection, and transaction management. In this paper, we shift focus to a critical yet underexplored area: data management for quantum computing. We are currently in the noisy intermediate-scale quantum (NISQ) era, where qubits, while promising, are fragile and still limited in scale. After differentiating quantum data from classical data, we outline current and future data management paradigms in the NISQ era and beyond. We address the data management challenges arising from the emerging demands of near-term quantum computing. Our goal is to chart a clear course for future quantum-oriented data management research, establishing it as a cornerstone for the advancement of quantum computing in the NISQ era.
The exponential growth of data in various domains has underscored the need for efficient unsupervised learning algorithms to discover hidden patterns and structures within large datasets. Traditional clustering algorithms often face challenges in handling dynamic datasets and require periodic retraining, making them less suitable for real-time applications. In response to these challenges, this research introduces a Quantum Incremental Clustering Algorithm System (QICAS) designed to adapt to evolving datasets and provide timely insights into the underlying structures. The proposed QICAS is based on quantum computing principles, leveraging the unique properties of qubits and quantum superposition to perform clustering tasks more efficiently than classical counterparts. The research focuses on the planning, design, and execution of the QICAS, ensuring a comprehensive understanding of the algorithm's capabilities and performance.
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.