Quantum computing has rapidly gained prominence for its unprecedented computational efficiency in solving specific problems when compared to classical computing counterparts. This surge in attention is particularly pronounced in the realm of quantum machine learning (QML) following a classical trend. Here we start with a comprehensive overview of the current state-of-the-art in Quantum Support Vector Machines (QSVMs). Subsequently, we analyze the limitations inherent in both annealing and gate-based techniques. To address these identified weaknesses, we propose a novel hybrid methodology that integrates aspects of both techniques, thereby mitigating several individual drawbacks while keeping the advantages. We provide a detailed presentation of the two components of our hybrid models, accompanied by the presentation of experimental results that corroborate the efficacy of the proposed architecture. These results pave the way for a more integrated paradigm in quantum machine learning and quantum computing at large, transcending traditional compartmentalization.
Quantum computing has rapidly gained prominence for its unprecedented computational efficiency in solving specific problems when compared to classical computing counterparts. This surge in attention is particularly pronounced in the realm of quantum machine learning (QML) following a classical trend. Here we start with a comprehensive overview of the current state-of-the-art in Quantum Support Vector Machines (QSVMs). Subsequently, we analyze the limitations inherent in both annealing and gate-based techniques. To address these identified weaknesses, we propose a novel hybrid methodology that integrates aspects of both techniques, thereby mitigating several individual drawbacks while keeping the advantages. We provide a detailed presentation of the two components of our hybrid models, accompanied by the presentation of experimental results that corroborate the efficacy of the proposed architecture. These results pave the way for a more integrated paradigm in quantum machine learning and quantum computing at large, transcending traditional compartmentalization.
Quantum Machine Learning represents a paradigm shift at the intersection of Quantum Computing and Machine Learning, leveraging quantum phenomena such as superposition, entanglement, and quantum parallelism to address the limitations of classical approaches in processing high-dimensional and large-scale datasets. This survey provides a comprehensive analysis of Quantum Machine Learning, detailing foundational concepts, algorithmic advancements, and their applications across domains such as healthcare, finance, and quantum chemistry. Key techniques, including Quantum Support Vector Machine, Quantum Neural Network, Quantum Decision Trees, and hybrid quantum-classical models, are explored with a focus on their theoretical foundations, computational benefits, and comparative performance against classical counterparts. While the potential for exponential speedups and enhanced efficiency is evident, the field faces significant challenges, including hardware constraints, noise, and limited qubit coherence in the current era of Noisy Intermediate-Scale Quantum devices. Emerging solutions, such as error mitigation techniques, hybrid frameworks, and advancements in quantum hardware, are discussed as critical enablers for scalable and fault-tolerant Quantum Machine Learning systems. By synthesizing state-of-the-art developments and identifying research gaps, this survey aims to provide a foundational resource for advancing Quantum Machine Learning toward practical, real-world applications in tackling computationally intensive problems.
The burgeoning field of machine learning has transformed numerous sectors, revolutionizing everything from image recognition to financial forecasting. However, classical machine learning algorithms often encounter limitations when dealing with complex, high-dimensional problems. This is where the nascent field of quantum machine learning (QML) emerges, offering a paradigm shift with its unique computational capabilities. By harnessing the principles of quantum mechanics, QML promises to solve problems intractable for classical methods, like simulating complex molecules or optimizing financial portfolios. However, current quantum hardware limitations necessitate a hybrid approach: Hybrid Quantum-Classical Machine Learning Models (HQCLML). The convergence of quantum computing and classical machine learning has sparked significant interest in the development of hybrid quantum-classical machine learning models. This research explores the synergy between quantum and classical paradigms, aiming to leverage the strengths of both to enhance the capabilities of machine learning algorithms. The paper provides an in-depth overview of quantum computing principles, classical machine learning models, and the foundational concepts that form the basis for hybrid models. Various approaches to integrating quantum computing into machine learning are discussed, emphasizing the potential advantages in solving complex problems, particularly those involving large-scale optimization or exponential search spaces. The study delves into quantum machine learning algorithms, showcasing examples such as Quantum Support Vector Machines and Quantum Neural Networks. Case studies and applications of hybrid models are presented to illustrate instances where quantum enhancements outperform classical counterparts. While highlighting the promising achievements, the paper also addresses the current challenges and limitations associated with hybrid models, including practical considerations, error rates, and the impact of decoherence in quantum computing. As quantum hardware technologies continue to advance, the paper explores the current landscape of quantum processors and their implications for hybrid models. The discussion extends to future directions, offering predictions for the development of hybrid quantum-classical machine learning models. Emerging technologies and potential breakthroughs are considered, presenting a forward-looking perspective on the evolving landscape of artificial intelligence research. In conclusion, the research underscores the significance of hybrid quantum-classical machine learning models as a transformative avenue for addressing complex computational problems. The synergy between quantum and classical approaches holds immense potential for advancing the field of machine learning, opening new horizons for solving problems that were once deemed computationally intractable.
Quantum machine learning is one of the fields where quantum computers are expected to bring advantages over classical methods. However, the limited size of current computers restricts the exploitation of the full potential of quantum machine learning methods. Additionally, different computing paradigms, both quantum and classical, each have their own strengths and weaknesses. Obtaining optimal results with algorithms thus requires algorithms to be tweaked to the underlying computational paradigm, and the tasks to be optimally distributed over the available computational resources. In this work, we explore the potential gains from combining different computing paradigms to solve the complex task of data classification for three different datasets. We use a gate-based quantum model to implement a quantum kernel and implement a complex feature map. Next, we formulate a quadratic unconstrained optimisation problem to be solved on quantum annealing hardware. We then evaluate the losses on classical hardware and reconfigure the model parameters accordingly. We tested this so-called triple-hybrid quantum support vector machine on various data sets, and find that it achieves higher precision than other support vector machines (both quantum and classical) on complex quantum data, whereas it achieves varying performance on simple classical data using limited training. For the complex data sets, the triple-hybrid version converges faster, requiring fewer circuit evaluations.
Quantum computing has demonstrated remarkable prowess in addressing factorization issues and unordered search problems, showcasing its quantum parallelism capabilities that enable exponential speed-up for specific computational challenges. However, the seamless integration of classical and quantum computing to harness accelerated computation speed presents unique challenges. This paper delves into the intricacies of this integration, focusing on the current state of quantum machine learning (QML) and evaluating the performance of classical and quantum algorithms in terms of accuracy. To address these challenges, we conducted experiments utilizing three datasets for binary classification, implementing both classical Support Vector Machine (SVM) and Quantum SVM (QSVM) algorithms. Our investigations reveal that the QSVM algorithm consistently outperforms its classical counterpart on complex datasets. Notably, the performance gap between quantum and classical models widens with increasing dataset complexity, shedding light on the susceptibility of simple classical models to overfitting when confronted with intricate datasets.Despite the ongoing development required for quantum hardware with ample resources, our findings underscore the significant potential of quantum machine learning, particularly in unsupervised learning and generative models. As we move forward, it is imperative to channel more efforts into exploring novel quantum learning models capable of harnessing the inherent power of quantum mechanics to overcome the constraints of classical machine learning. This research contributes to the ongoing discourse on the future of quantum computing, emphasizing the need for continued exploration and innovation in quantum machine learning methodologies.
As the limits of classical machine learning (ML) become increasingly evident—especially in terms of computational scalability and data processing—quantum computing emerges as a promising frontier capable of addressing these challenges. Quantum Machine Learning (QML) represents the intersection of quantum computing and traditional ML, aiming to leverage quantum phenomena such as superposition, entanglement, and quantum parallelism to enhance learning capabilities, accelerate training, and solve complex problems that are intractable for classical algorithms. This paper provides a comprehensive overview of the evolving field of QML, beginning with the theoretical foundations of quantum computing and its synergy with classical machine learning principles. We explore key QML algorithms, including quantum support vector machines (QSVM), variational quantum classifiers (VQC), quantum neural networks (QNN), and hybrid quantum-classical approaches that exemplify the practical integration of both paradigms. We further examine real-world applications of QML across domains such as drug discovery, finance, climate modeling, and optimization, where quantum enhancements have begun to show early but promising results. Despite its theoretical potential, QML faces substantial limitations, including quantum hardware instability, limited qubit scalability, noise sensitivity, and unresolved challenges in quantum data encoding and interpretability. In synthesizing recent advancements and persistent obstacles, this paper highlights the importance of continued interdisciplinary research, algorithmic refinement, and hardware evolution. We also outline future directions that could lead QML from experimental promise to practical utility, including developments in quantum error correction, benchmarking standards, and application-specific quantum architectures
Quantum computing has emerged as a promising technology capable of solving complex computational problems more efficiently than classical computers. Among the various quantum algorithms developed, the Quantum Support Vector Machine (QSVM) has gained significant attention for its potential to enhance machine learning tasks, particularly classification. This review paper explores the theoretical foundations, methodologies, and potential advantages of QSVM for classification tasks. We discuss the quantum computing principles underpinning QSVM, compare them with classical support vector machines, and review recent advancements and applications. Finally, we highlight the challenges and prospects of QSVM in the context of quantum machine learning.