Quantum computing is an emerging technology that has the potential to change human history. In this thesis, we propose to dive deep into this new technology through theory background and computational experimentation. We explain the theoretical background needed to understand how quantum computing works, then we use this theory background to make a computational implementation on the AWS cloud using Amazon Braket (i.e. AWS service to use quantum processors). As the result of this project, we deliver a document with the theory background required and the Python- code that implements PCA using Amazon Braket. Computational results are consistent with the theory background described in the thesis.
Quantum computing is an emerging technology that has the potential to change human history. In this thesis, we propose to dive deep into this new technology through theory background and computational experimentation. We explain the theoretical background needed to understand how quantum computing works, then we use this theory background to make a computational implementation on the AWS cloud using Amazon Braket (i.e. AWS service to use quantum processors). As the result of this project, we deliver a document with the theory background required and the Python- code that implements PCA using Amazon Braket. Computational results are consistent with the theory background described in the thesis.
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 research project embarks on a comprehensive comparative analysis of classical and quantum machine learning models specifically tailored for time series forecasting. By employing a range of classical machine learning models alongside quantum machine learning models, the study aims to predict future data points with heightened accuracy and efficiency. Leveraging AWS Braket, the project facilitates the deployment of quantum models on both quantum simulators and quantum hardware devices, providing a robust platform for empirical evaluation. The goal is to meticulously assess the performance metrics, computational efficiency, and predictive accuracy of these models. Ultimately, the research aspires to enhance the understanding and practical application of quantum machine learning techniques in the realm of time series forecasting, potentially uncovering new paradigms in predictive analytics. Our research indicates that the performance metrics for classical machine learning models yielded superior results compared to those of quantum machine learning models for our use case, although when ensembled with classical models, we get better results.
Information processing with electronic devices has been the core of the development of technology for the last 60 years, having improvements, new applications, and advances year by year. With this search for new and more advanced ways to process information, a new concept appeared 40 years ago: Quantum computing, which has ceased to be just a concept and has become a reality in recent years, opening the door to new applications, implementations, algorithms, and methods for the development of new and more advanced technologies, with Quantum information science at the core. Quantum machine learning is one of these new fields of study that investigates the interaction of concepts from quantum computation and machine learning. The paradigm of quantum computing and artificial intelligence has been growing steadily in recent years and given the potential of this technology by recognizing the com- puter as a physical system that can take advantage of quantum mechanics for solving problems faster, more efficiently, and accurately, in this work, we present three imple- mentations of applied quantum machine learning: in parallel quantum computation to evaluate the speed-ups of quantum models, in cybersecurity systems for intrusion detection and the implementation of the algorithm quantum Fourier transform for quantum machine learning tasks. The results not only show how successfully quan- tum machine learning can solve current problems but also, progress on how these systems can significantly improve their performance, to achieve Quantum advantage over classical systems. All the experiments presented in this work were executed either on a Quantum simulator or real Quantum Hardware accessible from the plat- forms and frameworks from IBM Quantum, Amazon Web Services, and Xanadu Quantum Technologies Inc.
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.
The convergence of artificial intelligence (AI), quantum computing, and cloud infrastructure represents a paradigm shift in how intelligent systems are designed, trained, and deployed. As AI models continue to demand exponential computational resources, classical systems are increasingly reaching their performance limits in domains such as high-dimensional optimization, complex pattern recognition, and probabilistic modeling. Quantum computing, with its foundational principles of superposition, entanglement, and quantum parallelism, offers a theoretical edge in solving problems that are intractable for conventional hardware. However, the practical deployment of quantum algorithms—particularly those relevant to machine learning (ML) has been hindered by the limited scalability and accessibility of quantum hardware. To bridge this gap, cloud-based quantum computing platforms have emerged, providing remote, programmable access to quantum devices and hybrid runtimes. In this context, the present study offers a comprehensive review of cloud-delivered quantum AI systems, examining core architectures, hybrid quantum-classical algorithms, platform capabilities, and real-world use cases. The paper synthesizes recent advances and performance insights from systems such as Amazon Braket, IBM Quantum, and Azure Quantum, while critically analyzing algorithmic trends like variational quantum circuits and quantum kernel methods. It further explores challenges including circuit depth limitations, latency in hybrid models, and standardization barriers across toolchains. By consolidating current research and implementation patterns, this review aims to inform the design and future trajectory of scalable, cloud-native quantum AI applications.
The intersection of machine learning and quantum computing has been one of the greatest scientific projects in current time. With quantum computing in the offing, the assurance of using quantum mechanical effects in computational learning to transform the world has never been closer. The publication of this book comes at a critical time, when theoretical models are shouldering maturity with capabilities of experimentation, that has brought about great possibilities to implement computational benefits that quantum systems can provide in performing machine learning tasks. Quantum machine learning is one field which has rapidly changed in the last ten years. What was originally viewed as mere theoretical conjecture has now evolved into a living research community which includes rigorous mathematical modeling, novel algorithm design, and more and more complex experimental realisations on the near-term quantum computers. Such change can be characterised by the increasing sophistication of our theory in as well as the revolutionary advances in quantum computing technology, and by the noisy intermediate-scale quantum (NISQ) era. Chapters in this book chronologically discuss the complex nature of quantum machine learning, including theoretical principles and challenges of practice. All the chapters cover a very important aspect in this growing area leaving the reader not only with an in-depth analysis but also a wide enough point of view to traverse this somewhat complicated inter-disciplinary area. The book is organized in such a way that that it takes the reader through more advanced parts of the quantum machine learning theory and practice. We start by explaining quantum feature maps, and kernel-type methods and laying the mathematical background on which quantum-enhanced learning is based. There we discuss computational complexity, trainability issues, neural network architectures, scaling of resources, sample complexity limits and the issue critical of quantum data encoding. This book has a variety of audience. The works will also offer exhaustive proposals of key concepts, methodologies and open problems to graduate students and researchers venturing into the field. Individuals who operate quantum computing platforms will have an understanding of the options available when it comes to designing an algorithm and the resources needed, as well as the expectations of its performance. The systematic approach of tackling complexity theorist-foundations and rigorous mathematical analyses will be appreciated by the theorists. Quantum machine learning is interdisciplinary and requires the services and knowledge of quantum physics, computer science, mathematics, and statistics. Although we do not expect our readers to have the basic knowledge of quantum computing and classical machine learning, we give adequate background and references to accommodate other readers of varying technical background. By 2026, quantum machine learning will be on a important crossroads. NISQ has also provided its own stunning demonstrations and harsh lessons on the difficulty of obtaining useful quantum advantage. Other phenomena like barren plateaus have changed the minds of us on the nature of variational quantum algorithm trainability. Resource-efficient quantum learning has been transformed by the advanced measurement protocols, such as classical shadow tomography. The frontier of the implementable quantum algorithms is constantly being pushed by hardware advances. The book reflects the picture of the field in this dynamic time, including the latest theoretical developments, their experiments, and new optimal practices. We admit that quantum machine learning is a fast-developing science, and there are certain spheres of our knowledge that will surely become more profound in the nearest future. The transformative potential of quantum machine learning is still achievable only with further achievements in many dimensions: the theory of when and why quantum algorithms are effective, algorithmic innovations to overcome the limitations of near-term hardware, hardware engineering to overcome qubit quality and system scale, and discovering applications when quantum algorithms and methods and methods offer significant superior practical performance. Hopefully, this book will provide useful guidance to all the existing knowledge and guide to the future innovations. The opportunities are out of the world, yet the challenges are significant. With the ongoing development of quantum hardware capabilities and an ever-increasing theoretical basis, it is quite possible that quantum machine learning will transform the world of approaching the problem of complex computational learning.