Quantum Machine Learning (QML), the intersection of quantum computing and machine learning, has been thought to be one of the more promising ways to achieve quantum advantage in the immediate future. Applying these well-defined models in practical settings comes with various restrictions that are dependent on either the underlying hardware or the mathematical formalization of the model itself. In this thesis, we study these two classes of issues, find solutions to them in different contexts, and then generalize the common elements these issues and solutions have. In the case of hardware-related issues, the key observation is that the hardware features, like qubit counts, gate topology, or error rates, tend to limit the size of the problem we want to solve. These issues can usually be solved by optimizing the model's structure to make it more compatible with the hardware on which it is run. This holds with gate-based approaches, like how we used quantum compilation routines to optimize an existing quantum linear regression routine to be runnable in existing quantum hardware, but also with quantum annealing hardware, like how we leveraged classical software techniques to improve the efficiency of embedding a restricted Boltzmann machine model to a quantum annealing device. Issues arising from the model construction process tend to be less uniform and are highly dependent on the model itself. The model formalization can give rise to unintended phenomena, which can be alleviated either by accounting for them during use or by adjusting the model itself to negate the issue. We found examples of these issues emerging in existing QML models, such as the phenomenon of self-erasing gates in the established way of constructing quantum embedding kernels, and how the permutation of features can have a considerable impact on the performance of variational QML models. We also developed solutions to these issues in the form of improvements on the models themselves or as best practices on how to circumvent them. While the research topic of this thesis is incredibly vast, our research is a step towards more practical QML models. It encompasses many distinct QML models and hardware technologies on which these models can be run. By developing insight into how QML algorithms operate in practical settings, we also develop readiness for a possible future in which quantum computers are a viable option for machine learning tasks.
Quantum Machine Learning (QML), the intersection of quantum computing and machine learning, has been thought to be one of the more promising ways to achieve quantum advantage in the immediate future. Applying these well-defined models in practical settings comes with various restrictions that are dependent on either the underlying hardware or the mathematical formalization of the model itself. In this thesis, we study these two classes of issues, find solutions to them in different contexts, and then generalize the common elements these issues and solutions have. In the case of hardware-related issues, the key observation is that the hardware features, like qubit counts, gate topology, or error rates, tend to limit the size of the problem we want to solve. These issues can usually be solved by optimizing the model's structure to make it more compatible with the hardware on which it is run. This holds with gate-based approaches, like how we used quantum compilation routines to optimize an existing quantum linear regression routine to be runnable in existing quantum hardware, but also with quantum annealing hardware, like how we leveraged classical software techniques to improve the efficiency of embedding a restricted Boltzmann machine model to a quantum annealing device. Issues arising from the model construction process tend to be less uniform and are highly dependent on the model itself. The model formalization can give rise to unintended phenomena, which can be alleviated either by accounting for them during use or by adjusting the model itself to negate the issue. We found examples of these issues emerging in existing QML models, such as the phenomenon of self-erasing gates in the established way of constructing quantum embedding kernels, and how the permutation of features can have a considerable impact on the performance of variational QML models. We also developed solutions to these issues in the form of improvements on the models themselves or as best practices on how to circumvent them. While the research topic of this thesis is incredibly vast, our research is a step towards more practical QML models. It encompasses many distinct QML models and hardware technologies on which these models can be run. By developing insight into how QML algorithms operate in practical settings, we also develop readiness for a possible future in which quantum computers are a viable option for machine learning tasks.
Quantum Machine Learning (QML) integrates quantum algorithms into machine learning programs to enhance and expedite classical machine learning techniques. It leverages quantum data and hybrid quantum-classical models, utilizing the principles of superposition and entanglement to handle complex joint probability distributions that would require exponential classical computational resources. QML aims to harness the unique properties of quantum computers for practical machine learning tasks, with the goal of achieving quantum advantage over classical approaches. QML finds applications in diverse fields such as chemical and quantum matter simulation, quantum control, quantum communication networks, and quantum metrology. Quantum algorithms can analyze quantum states and improve computational speed and data storage. Two main frameworks, quantum kernel methods and variational quantum algorithms, are widely used due to their implement ability on quantum hardware and capacity to work on general datasets. Quantum computers can solve linear algebraic problems, calculate eigenvectors and eigenvalues, and perform support vector algorithms at exponentially faster rates than classical computers. Despite its potential, QML faces significant challenges related to quantum hardware capabilities, including limited qubit connectivity, noise, coherence times, and errors in state preparation and measurement. Hardware constraints, compatibility issues, high computational and memory requirements, and the difficulty of quantum programming also pose obstacles. Overcoming these hardware and software limitations is crucial for realizing the full potential of quantum machine learning
Quantum machine learning (QML) is an emerging interdisciplinary field which incorporates some features of quantum computing with machine learning. Quantum hardware development has sparked research interest for AI scientists because it gives capabilities in high-dimensional data processing and simultaneous operation execution. The investigation within this paper traces the main features of QML by reviewing variational quantum classifiers (VQCs) as well as quantum kernels and hybrid quantum-classical models. VQCs serve as quantum parameterized circuits whose optimization process with classical feedback achieves quantum superiority for classification applications. Quantum kernels expand Hilbert space features to improve traditional kernel methods, thus proving their functionality in quantum feature space. Hybrid approaches unite NISQ hardware with classical systems, which makes QML applicable to real-world applications right now. (Rietsche et al., 2022; Tychola et al., 2023) An analysis explores separate functions and combined effects of these components, which enhance model performance, extend generalization, and boost computational efficiency. The paper discusses necessary knowledge first, along with existing applications, before analyzing them against traditional methods. The paper reviews current QML tools while exploring their operational readiness as well as practical issues and deployment barriers for broader adoption. The paper conducts an in-depth investigation of significant limitations that include hardware noise alongside questions regarding scalability and interpretability. This paper shows how QML will transform machine learning applications through its review of obstacles that must be resolved to achieve its complete potential development. The study presents researchers and practitioners with an extensive comprehension of QML developments and emerging paths for this revolutionary field.
Recent advances in Quantum Machine Learning (QML) bring together Quantum Computing and Machine Learning with the aim of enhancing the advantages of Machine Learning to solve problems efficiently. The properties of quantum systems (superposition, entanglement, etc.) to represent multiple states of information at once, QML can potentially offer significant advantages when solving complex high-dimensional (multidimensional) computational problems. This paper reviews the current state-of-art regarding QML in general through an organized compilation and assessment of selected studies recently published in scientific literature that were found via large electronic library catalogs based on peer-reviewed sources according to algorithms used, optimization approaches employed, and areas of applied study. This review provides comparative analyses of each selected studies for purposes of identifying major trends in QML research/practice as well as opportunities for methodological improvements and challenges still existing within QML. Finally, this review presents an overview of Quantum Computing and Quantum Data Encoding Principles utilized to translate Classical Data into Quantum States as a high-level evaluation of several popular Quantum Learning Algorithms (e.g., Quantum Neural Networks, Quantum Support Machines, and Variational Quantum Circuit). The survey describes the optimization strategies such as gradient based techniques, Bayesian Optimization and evolutionary techniques being used to improve the stability of the quantum machine learning (QML) training process. Then, it provides examples of application areas for such QML, where healthcare analytics, environmental modelling, manufacturing optimization and analysis of biologic data are included. The comparative review of the recent academic research has identified several limitations and challenges regarding the use of QML that researchers. Although QML is believed to be capable of solving complex computational problems, issues such as noise in hardware, limited numbers of qubits and scalability present significant challenges for researchers working with this type of machine learning application. Overall, it appears that this survey provides a broad overview of contemporary developments and future trends for developing high-performance quantum learning systems.
Quantum Machine Learning (QML) is an advanced discipline that emerges from the combined power of machine learning and quantum computing that has the ability to address intricate challenges in several domains. The domain of quantum machine learning investigates the development and execution of quantum software with the potential to facilitate machine learning at a much superior pace compared to traditional computers. This research delves into the fundamental principles of quantum mechanics and their crucial role in quantum computing, emphasizing the potential of various quantum algorithms to surpass classical algorithms in specific computational tasks, and then methodically navigates through the quantum machine learning algorithms, offering profound insights into their application potential in revolutionizing data analysis and complex problem-solving methodologies, including their importance in the Language Learning Models (LLM) and Language Analysis Models (LAM). The study also provides insights into the various quantum platforms, encompassing both hardware and software aspects for the implementation of QML algorithms, and also explores the challenges prevalent in QML, with a particular focus on the limitations imposed by existing quantum hardware and the intricate nuances of data processing within quantum frameworks. This study contributes by presenting the basis for future research work related to the development of algorithms in the field of quantum machine learning and anticipating the far-reaching impact of QML across diverse scientific and technological domains.
Quantum Computing (QC) claims to improve the efficiency of solving complex problems, compared to classical computing. When QC is integrated with Machine Learning (ML), it creates a Quantum Machine Learning (QML) system. This paper aims to provide a thorough understanding of the foundational concepts of QC and its notable advantages over classical computing. Following this, we delve into the key aspects of QML in a detailed and comprehensive manner. In this survey, we investigate a variety of QML algorithms, discussing their applicability across different domains. We examine quantum datasets, highlighting their unique characteristics and advantages. The survey also covers the current state of hardware technologies, providing insights into the latest advancements and their implications for QML. Additionally, we review the software tools and simulators available for QML development, discussing their features and usability. Furthermore, we explore practical applications of QML, illustrating how it can be leveraged to solve real-world problems more efficiently than classical ML methods. This survey aims to consolidate the current landscape of QML and outline key opportunities and challenges for future research.
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