Quantum technologies can provide innovative solutions to many complex problems, and thus quantum machine learning has taken a unique place in the world of computing. Quantum technology reaches an advanced level when the potential of quantum computing features is used for machine learning. Applying quantum computing features in traditional algorithms provides an exceptional parallel computing capability for solving complex problems. The essence of this paper is a comparative study of the basic concepts of quantum computing and their superior capabilities over classical computing. This article describes the application based algorithms such as QSVM, QPCA, and Q-KNN along with Grover's algorithm, which is the most popular and fundamental quantum machine learning algorithm. This study aims to understand various learning models that incorporate the advantages of computing into quantum circuits for enhancing classical machine learning functionalities.
Quantum technologies can provide innovative solutions to many complex problems, and thus quantum machine learning has taken a unique place in the world of computing. Quantum technology reaches an advanced level when the potential of quantum computing features is used for machine learning. Applying quantum computing features in traditional algorithms provides an exceptional parallel computing capability for solving complex problems. The essence of this paper is a comparative study of the basic concepts of quantum computing and their superior capabilities over classical computing. This article describes the application based algorithms such as QSVM, QPCA, and Q-KNN along with Grover's algorithm, which is the most popular and fundamental quantum machine learning algorithm. This study aims to understand various learning models that incorporate the advantages of computing into quantum circuits for enhancing classical machine learning functionalities.
Quantum computation and quantum information have sparked tremendous interest across a wide range of scientific disciplines, from physics to chemistry and engineering, as well as computer science, mathematics, and statistics. Data science is the application of statistical methodologies, computer algorithms, and domain scientific data to extract knowledge and insights from large amounts of data and solve complicated real-world issues. As a result, quantum machine learning has carved itself a distinct niche in the world of computers. When the potential of quantum computing characteristics is employed for machine learning, quantum technology advances to an advanced degree. When quantum computing capabilities are incorporated into standard methods, they give extraordinary parallel computing power for addressing complicated problems. The core of this work is a comparison of the fundamental principles of quantum computing and their superiority over traditional computing. This paper discusses application-based algorithms including QSVM, QPCA, and Q-KNN, as well as Grover's algorithm, the most common and foundational quantum machine learning technique.
Two of the most disruptive technologies of the 21st century are quantum computing and artificial intelligence. Their intersection has led to the emergence of a new discipline referred to as Quantum Machine Learning (QML), which aims to enhance the capabilities of classical machine learning by leveraging the computational advantages of quantum devices. This paper provides a survey of the most advanced Quantum Machine Learning (QML) algorithms, including Quantum Support Vector Machines (QSVMs), Quantum k-nearest Neighbours (QkNN), Quantum Principal Component Analysis (QPCA), Quantum Neural Networks (QNNs), and Quantum Reinforcement Learning (QRL). The theoretical and practical status, as well as the empirical performance, of these algorithms, were summarised using a structured review method. The findings reveal a potential for speed-ups in classification, clustering, and optimisation among a range of applications, particularly for perfect quantum systems. However, hardware constraints, software irregularities, and training issues, such as barren plateaus, have limited the practical utility of this approach. Applications of QML in areas such as disaster preparedness and management, drug discovery, environmental sustainability, urban planning methodology, NLP, and finance demonstrate both the potential and current limitations of QML, with most applications still at the proof-of-concept level. In this review, we conclude that QML could be revolutionary, but its feasibility ultimately relies on improvements in physical hardware, the robustness of algorithms, and the standardisation of benchmarks.
Quantum computing has been proven to excel in factorization issues and unordered search problems due to its capability of quantum parallelism. This unique feature allows exponential speed-up in solving certain problems. However, this advantage does not apply universally, and challenges arise when combining classical and quantum computing to achieve acceleration in computation speed. This paper aims to address these challenges by exploring the current state of quantum machine learning and benchmarking the performance of quantum and classical algorithms in terms of accuracy. Specifically, we conducted experiments with three datasets for binary classification, implementing Support Vector Machine (SVM) and Quantum SVM (QSVM) algorithms. Our findings suggest that the QSVM algorithm outperforms classical SVM on complex datasets, and the performance gap between quantum and classical models increases with dataset complexity, as simple models tend to overfit with complex datasets. While there is still a long way to go in terms of developing quantum hardware with sufficient resources, quantum machine learning holds great potential in areas such as unsupervised learning and generative models. Moving forward, more efforts are needed to explore new quantum learning models that can leverage the power of quantum mechanics to overcome the limitations of classical machine learning.
There has been remarkable development in the data-driven decision-making domain of Artificial Intelligence (AI). However, the complexity of the AI models and the amount of data have shown some drawbacks with conventional computing models. One of the interdisciplinary branches of quantum computing and machine learning is Quantum Machine Learning (QML), which is a combination of quantum computing and machine learning and relies on the unique characteristics of quantum computing, such as superposition, entanglement, and quantum parallelism, to enhance the performance of the machines. This review provides a comprehensive description of theory, architectures, algorithms, implementational approaches, applications, problems, and future directions of QML. The core concepts of quantum computing and machine learning are introduced. Different representations of quantum states, quantum data encoding, variational quantum circuits, quantum kernel methods, and hybrid quantum or classical optimization are discussed. Significant QML algorithms like Quantum Support Vector Machines, Quantum Neural Networks, Variational Quantum Eigen solvers, Quantum Approximate Optimization Algorithms, and Quantum reinforcement learning are explored and evaluated for computational power and applications. The scaling up of quantum computer sizes, data encoding overhead, complexity of optimizations, benchmarking, and decoherence are discussed in detail, as well as recent developments in fault-tolerant quantum computing and hybrid learning approaches. Finally, a conceptual framework is given to demonstrate the unification of classical computation and quantum computation to intelligently process data. This review demonstrates how QML could be the next-generation computational method to accelerate scientific advancements and enable new advanced AI technologies.
Quantum computing, with its foundational principles of superposition and entanglement, has the potential to provide significant quantum advantages, addressing challenges that classical computing may struggle to overcome. As data generation continues to grow exponentially and technological advancements accelerate, classical machine learning algorithms increasingly face difficulties in solving complex real-world problems. The integration of classical machine learning with quantum information processing has led to the emergence of quantum machine learning, a promising interdisciplinary field. This work provides the reader with a bottom-up view of quantum circuits starting from quantum data representation, quantum gates, the fundamental quantum algorithms, and more complex quantum processes. Thoroughly studying the mathematics behind them is a powerful tool to guide scientists entering this domain and exploring their connection to quantum machine learning. Quantum algorithms such as Shor’s algorithm, Grover’s algorithm, and the Harrow–Hassidim–Lloyd (HHL) algorithm are discussed in detail. Furthermore, real-world implementations of quantum machine learning and quantum deep learning are presented in fields such as healthcare, bioinformatics and finance. These implementations aim to enhance time efficiency and reduce algorithmic complexity through the development of more effective quantum algorithms. Therefore, a comprehensive understanding of the fundamentals of these algorithms is crucial.
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.