Machine learning (ML) algorithms have gained great success during the last 20 years. However, this development is encountering growing difficulties. With dataset sizes continually increasing and Moore's Law coming to an end, it is predicted that soon a stage will arrive where present computing tools will no longer suffice. With quantum computing technology approaching the point of commercialization enabling quantum supremacy, they can efficiently tackle certain challenges in machine learning that are thought to be difficult for conventional machines. This paper details how quantum machine learning works, with special attention to the quantum support vector classifier. This work also brings out the comparison between the Quantum support vector classifier and its classical equivalent by working on the Cleveland heart disease dataset.
Machine learning (ML) algorithms have gained great success during the last 20 years. However, this development is encountering growing difficulties. With dataset sizes continually increasing and Moore's Law coming to an end, it is predicted that soon a stage will arrive where present computing tools will no longer suffice. With quantum computing technology approaching the point of commercialization enabling quantum supremacy, they can efficiently tackle certain challenges in machine learning that are thought to be difficult for conventional machines. This paper details how quantum machine learning works, with special attention to the quantum support vector classifier. This work also brings out the comparison between the Quantum support vector classifier and its classical equivalent by working on the Cleveland heart disease dataset.
Machine Learning (ML) has been extensively utilized in various scientific and engineering domains. But the inherent constraints and computational complexity that arise in classical machine learning are particularly evident when dealing with large-scale, high-dimensional datasets or when attempting to solve intricate optimisation problems. Quantum systems exhibit unconventional patterns that are typically not effectively generated by traditional systems and can also tackle high dimensional and large size datasets efficiently. Therefore, it is believed that quantum computing systems may outperform traditional computing devices in machine learning tasks. Quantum Machine Learning (QML) has emerged as a rapidly developing field that combines the concepts of quantum mechanics and machine learning to solve complex problems across various disciplines. 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. However, the major challenges encountered are hardware limitations, noise, and development of quantum algorithms. The objective of this paper is to provide an insight on the foundational principles, essential algorithms, various associated platforms, potential applications and challenges being faced by QML. As data continues to grow in size and complexity, together with the emergence of nonlinear and high-dimensional real-world situations, the computing benefits offered by QML have the potential to facilitate significant advancements in the fields of healthcare, finance, robotics, logistics, communication, and cyber security. This paper presents the basis for future research work related to development of quantum algorithms in the domain of machine learning.
The emerging field of Quantum Machine learning (QML), which offers noticeably faster processing speed, is the result of the merger of Quantum Computing (QC) and Machine Learning (ML). Superposition and Entanglement are the fundamental concepts of QC, which increases computational scalability. This comprehensive overview examines QML foundations, algorithms, applications, and challenges of QML. When applied to the Cleveland Heart Disease Dataset, the Quantum Support Vector Classifier (QSVC) achieves an accuracy of 0.75 whereas the Variational Quantum Classifier (VQC) yields an accuracy of 0.62, thus showing that QSVC outperforms VQC. The main issue in QML that calls for effort and investment is noise. QML has the potential to advance technology and has its application in a variety of domains.
Machine Learning (ML) has changed the way computers work by letting them learn from data and make smart choices. However, traditional ML techniques encounter scalability and efficiency constraints as datasets get larger and computational problems become more complicated[1]. These restrictions could be circumvented by the new paradigms that quantum computing offers by using entanglement and superposition [2], [3]. The combination of the Quantum computing with the Machine Learning is called the Quantum Machine Learning(QML), offers efficiency improvements, new algorithmic opportunities, and speedups in certain linear algebraic operations. So, this review dives deeply into quantum-classical algorithms, with a particular emphasis on the approaches like quantum neural network and quantum support vector machines. We examine computational complexity, architectural design, benchmarking findings, and real-world applications (such as optimization, cryptography, and classification). We also discuss important issues including scalability, data encoding, and noise, and we suggest future paths for error prevention and scalable structures.
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.
Effective and accurate diagnosis of diseases such as cancer, diabetes, and heart failure is crucial for timely medical intervention and improving patient survival rates. Machine learning has revolutionized diagnostic methods in recent years by developing classification models that detect diseases based on selected features. However, these classification tasks are often highly imbalanced, limiting the performance of classical models. Quantum models offer a promising alternative, exploiting their ability to express complex patterns by operating in a higher-dimensional computational space through superposition and entanglement. These unique properties make quantum models potentially more effective in addressing the challenges of imbalanced datasets. This work evaluates the potential of quantum classifiers in healthcare, focusing on Quantum Neural Networks (QNNs) and Quantum Support Vector Machines (QSVMs), comparing them with popular classical models. The study is based on three well-known healthcare datasets—Prostate Cancer, Heart Failure, and Diabetes.The results indicate that QSVMs outperform QNNs across all datasets due to their susceptibility to overfitting. Furthermore, quantum models prove the ability to overcome classical models in scenarios with high dataset imbalance. Although preliminary, these findings highlight the potential of quantum models in healthcare classification tasks and lead the way for further research in this domain.
In this chapter and the next one, we will explore the exciting areas of quantum machine learning and quantum deep learning. Machine learning and deep learning have seen great success in recent years because of the increase in the computational power at our disposal and because of the high-end research in these fields. Quantum machine learning presents an exciting opportunity to increase the computational efficiency of the existing machine learning algorithms as well as presents a way to tackle some of the more computationally intractable problems. In this chapter, we start with the Harrow-Hassidim-Lloyd algorithm, popularly known as HHL, which acts as the matrix inversion routine in the quantum computing domain. Hence, HHL will be the default choice for algorithms such as linear regression and least square support vector machines. Subsequently, we touch upon quantum linear regression and support vector machines in detail in this chapter. We will then move on to implementing quantum routines such as quantum dot product and quantum Euclidean distances since they are integral to several machine learning algorithms such as the k-means clustering and nearest neighbor methods. In this regard, we will implement the k-means clustering method in detail. Also, we will discuss how Grover’s algorithm can be used to optimize quantum objectives by illustrating its usage in the cluster assignment in the k-means algorithm. Principal component analysis is an important machine learning technique, and we will walk through its quantum implementation in great detail as well.