This study takes a closer look at how quantum computing can be used to run regression models, a common type of machine learning algorithm. Our goal is to understand whether today's quantum systems—ranging from open-source tools like Qiskit to commercial platforms such as AWS Braket, Microsoft Azure Quantum, and IBM Quantum—can effectively support these models in practice. To explore this, several quantum libraries and kernel methods are used to implement and test different versions of regression algorithms. Then compared the performance of these quantum models with traditional, classical ones to see how they stack up in terms of speed, accuracy and practical usability. Simulated results offer a balanced view of where quantum computing stands today: showing both its promise and the areas where it still has room to grow. Overall, this work aims to shed light on the real-world potential of quantum technology in machine learning applications.
This study takes a closer look at how quantum computing can be used to run regression models, a common type of machine learning algorithm. Our goal is to understand whether today's quantum systems—ranging from open-source tools like Qiskit to commercial platforms such as AWS Braket, Microsoft Azure Quantum, and IBM Quantum—can effectively support these models in practice. To explore this, several quantum libraries and kernel methods are used to implement and test different versions of regression algorithms. Then compared the performance of these quantum models with traditional, classical ones to see how they stack up in terms of speed, accuracy and practical usability. Simulated results offer a balanced view of where quantum computing stands today: showing both its promise and the areas where it still has room to grow. Overall, this work aims to shed light on the real-world potential of quantum technology in machine learning applications.
This thesis explores the integration of quantum computing into Machine Learning (ML), aiming to evaluate the effectiveness and limitations of quantum-enhanced models compared to classical approaches. As quantum technologies continue to develop, they offer the potential for solving complex computational problems more efficiently, particularly in fields requiring high-dimensional data processing. A literature review is conducted to contextualize current advancements in Quantum Machine Learning, highlighting theoretical benefits and existing practical limitations. This work investigates whether quantum methods, specifically the Quantum Support Vector Classifier (QSVC), can provide measurable improvements over its classical counterpart. The QSVC, implemented using IBM’s Qiskit ML library, replaces traditional kernel functions with quantum kernels computed via quantum circuits and feature maps, embedding classical data into a Hilbert space. The experimental portion of this thesis compares classical and quantum models on two distinct datasets: one from the healthcare domain and another from particle physics. Various parameters are tested in a constrained grid search due to the high computational demands of quantum circuits. Results indicate that while quantum models are significantly more resource-intensive, they can achieve comparable or even improved performance on data that has more complex relations between their variables. However, scalability remains a major challenge. This work concludes that although QSVC does not yet consistently outperform classical models, it offers advantages in specific contexts and lays the groundwork for identifying future use cases where quantum computing may offer a practical advantage in ML.
Quantum machine learning is an emerging Technology that is evolving rapidly and aims to translate classical machine learning models into a quantum context to be extended with the properties and applications of quantum mechanics. Despite the obvious advantage of being able to process large amounts of data in a short time, this integration between quantum and machine learning is not without challenges, especially when it comes to real datasets. In this paper, we aim to analyze the practical applicability of quantum machine learning approaches and to perform different experiments on generated and dedicated datasets that respect the quantum kernel training pattern, respectively, on real datasets, and to determine whether these can be processed to be suitable for quantum operations. To fully see the performance and potential advantage brought by the quantum component, a series of tests will be performed on both simulators and real hardware and it remains to be seen whether there is an improvement related to time complexity. Another goal based on the experiments carried out is to see on which types of datasets it is worth or justifiable to use quantum hardware at the expense of classical one. The novelty of this work lies in the use of a real-world dataset, and the experiments demonstrate that integrating quantum methods with classical processing yields higher accuracy compared to purely classical approaches.
Machine learning and quantum computing are both areas with considerable progress in recent years. The combination of these disciplines holds great promise for both research and practical applications. Recently there have also been many theoretical contributions of quantum machine learning algorithms with experiments performed on quantum simulators. However, most questions concerning the potential of machine learning on quantum computers are still unanswered such as How well do current quantum machine learning algorithms work in practice? How do they compare with classical approaches? Moreover, most experiments use different datasets and hence it is currently not possible to systematically compare different approaches. In this paper we analyze how quantum machine learning can be used for solving small, yet practical problems. In particular, we perform an experimental analysis of kernel-based quantum support vector machines and quantum neural networks. We evaluate these algorithm on 5 different datasets using different combinations of quantum feature maps. Our experimental results show that quantum support vector machines outperform their classical counterparts on average by 3 to 4% in accuracy both on a quantum simulator as well as on a real quantum computer. Moreover, quantum neural networks executed on a quantum computer further outperform quantum support vector machines on average by up to 5% and classical neural networks by 7%.
Quantum Computing is a fastly growing area with many applications, including quantum machine learning (QML). Due to the rapid increase of computational power, machine learning models based on artificial neural networks (ANN) have become highly effective. Even though classical machine learning models have been performing well, quantum computing with machine learning enhances the performance in multiple ways. This paper studies different aspects of quantum machine learning. It introduces quantum computing over classical computation, followed by the recent tools and techniques developed in the area. We look at multiple QML models like quantum kernel, quantum support vector machine (QSVM), etc. Finally, we present the literature survey to encourage researchers and academicians.
Machine learning techniques give impressive results in many areas. However, due to the physical limitation of integrated circuits which restricts their computational power growth, and the rapid advances in quantum computing, lots of research studies on quantum machine learning (QML) have been done recently. QML is a technique that uses quantum algorithms as parts of the implementation. Quantum algorithms use quantum mechanics and have the potential to outperform classical algorithms for a given problem. In this paper, three widely used machine learning algorithms are discussed and their quantum versions are presented, namely: quantum neural network, quantum autoencoder, and quantum kernel method. In addition, we discuss the potential capabilities of these QML algorithms and review recent work employing them. Moreover, a quantum neural network prototype is implemented using Qiskit as a proof of concept and tested on a real quantum computer. Empirical results show that quantum neural networks can be trained efficiently.
Quantum machine learning (QML) provides a transformative approach to data analysis by integrating the principles of quantum computing with classical machine learning methods. With the exponential growth of data and the increasing complexity of computational tasks, quantum algorithms offer tremendous advantages in terms of processing speed, memory efficiency, and the ability to resolve issues intractable for classical systems. In this work, the use of QML techniques for both supervised and unsupervised learning problems is explored. Quantum-enhanced models such Quantum Support Vector Machines (QSVMs) and Quantum Neural Networks (QNNs) show outstanding performance in classification and regression tasks by using quantum kernels and entanglement in supervised learning. Moreover, hybrid quantum-classical solutions offer useful implementations on noisy intermediate-scale quantum (NISQ) devices, hence bridging the gap between present quantum technology and practical uses. By means of comparative analysis, this paper emphasizes the possible benefits and drawbacks of QML, thereby providing understanding of its future importance in sectors including material science, finance, and healthcare. In the end, QML opens the path for a new era of intelligent data processing and solves until unthinkable difficult challenges.