Over the past decade, the field of Classical Machine Learning has experienced rapid growth, primarily driven by the widespread adoption of the internet and the increasing interconnectivity of digital devices.This expansion has led to the generation of vast volumes of data requiring effective analysis and processing.Machine Learning, as a subfield of Artificial Intelligence, leverages tools mainly from Statistics to identify patterns and extract meaningful insights from data without the need for explicit programming.At the same time, Quantum Computing introduces a fundamentally different paradigm for information processing, utilizing principles of Quantum Mechanics, such as superposition and entanglement.The convergence of these two scientific domains has given rise to the emerging field of Quantum Machine Learning.The present thesis aims to investigate whether the integration of Machine Learning and Quantum Computing can offer tangible advancements in the efficiency and performance of Artificial Intelligence systems.Based on current research findings, this study explores the potential, advantages, and limitations of this interdisciplinary approach, as well as its prospects for development in the coming years.
Over the past decade, the field of Classical Machine Learning has experienced rapid growth, primarily driven by the widespread adoption of the internet and the increasing interconnectivity of digital devices.This expansion has led to the generation of vast volumes of data requiring effective analysis and processing.Machine Learning, as a subfield of Artificial Intelligence, leverages tools mainly from Statistics to identify patterns and extract meaningful insights from data without the need for explicit programming.At the same time, Quantum Computing introduces a fundamentally different paradigm for information processing, utilizing principles of Quantum Mechanics, such as superposition and entanglement.The convergence of these two scientific domains has given rise to the emerging field of Quantum Machine Learning.The present thesis aims to investigate whether the integration of Machine Learning and Quantum Computing can offer tangible advancements in the efficiency and performance of Artificial Intelligence systems.Based on current research findings, this study explores the potential, advantages, and limitations of this interdisciplinary approach, as well as its prospects for development in the coming years.
Quantum computing has emerged as one of the most promising technologies capable of solving computational problems beyond the capabilities of classical computers. When integrated with Artificial Intelligence (AI), Quantum Machine Learning (QML) offers new possibilities for accelerating data processing, optimizing complex models, and improving predictive performance. Unlike conventional machine learning algorithms that rely solely on classical computation, QML leverages quantum phenomena such as superposition, entanglement, and quantum interference to perform computations more efficiently. This paper presents a comprehensive review of Quantum Machine Learning, discussing its architecture, core algorithms, applications, benefits, limitations, and future research directions. The study highlights the potential of QML in healthcare, cybersecurity, financial analytics, scientific research, and optimization problems. Additionally, the paper examines the challenges associated with quantum hardware, scalability, error correction, and algorithm development. The findings suggest that although Quantum Machine Learning remains in its early stages, it represents a transformative technology that may redefine intelligent computing over the coming decades.
A significant amount of interest has been generated in recent years in the convergence of quantum computing and data mining due to quantum algorithms' potential to revolutionize information extraction from vast datasets, as well as their ability to utilize quantum principles and natural capabilities to perform far more efficiently and efficiently than classic algorithms. We provide a comprehensive and detailed review of the current state of quantum computing algorithms in the field of data mining and machine learning, highlighting key developments, challenges, and their implementation. In particular, machine learning can benefit from QC mechanics like superposition and entanglement due to the use of q-bits as well as other superior advantages provided by quantum computing, such as Grover algorithm. It is important to note that classic machine learning models differ in terms of the types of learning, prediction models, and operating on data as well as performance over time, along with their counterparts, such as supervised and unsupervised models, which are naturally more sophisticated. Our review will also discuss how integrating and merging the two fields of machine learning and quantum computing, and their mechanics, will affect the aspect of time and resource
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.
Artificial Intelligence (AI) has been evolving at a very rapid pace, with increase in compute power, thus reshaping how complex problems are handled and interpreted from vast amounts of information. AI and newly emerging Quantum Computing have each made significant strides in recent years. Quantum computing has surfaced as a promising new paradigm that leverages on unique quantum phenomena such as like superposition and entanglement to potentially deliver dramatic gains in computational power. Increasingly, researchers are combining these distinct fields into "quantum AI," exploring ways to harness quantum devices for data-intensive tasks, advanced optimisation, and even tentative investigations into emergent machine "awareness." Already researchers have established the quantum advantage over classical AI. This paper outlines the fundamental aspects of AI with respect to quantum computing. It also surveys possible benefits for deep learning and optimization, and considers key concerns related to hardware readiness, algorithmic design, and ethical frameworks. By mapping current progress and pinpointing ongoing hurdles, we provide an entry-level guide to quantum AI's prospects and the steps required for its real-world deployment.
AI-powered machine vision has become a widely adopted technology in fields such as quality control, healthcare, and facial recognition, making it a standard option for many applications. Despite its widespread adoption, it continues to face several challenges. The primary barriers to its broader use in certain fields include its reliance on large datasets, lengthy training times, and high computational costs. To address these challenges, researchers are exploring quantum computing, which has the potential to process complex computations more efficiently than classical methods. Quantum computing leverages principles such as superposition, entanglement, and quantum encoding, enabling exponential speedup in computation and parallel processing of high-dimensional data, such as images.Since this research field is still relatively new and multiple approaches are being explored simultaneously, this paper aims to provide the reader with a comprehensive understanding of the current state of Quantum and Hybrid Quantum Machine Learning. It analyzes the different methodologies of quantum machine learning and presents their advantages and limitations. This should enable the reader to assess future developments in this field and offer insights into potential industrial applications.
This paper explores the convergence of quantum computing and artificial intelligence (AI), examining how their integration may redefine computational paradigms. Quantum computing, with its unique properties of superposition and entanglement, has the potential to exponentially accelerate AI processes, particularly in optimization, machine learning, and data analysis. We investigate quantum algorithms, such as the quantum Fourier transform and Grover’s algorithm, highlighting their application to AI models and machine learning tasks that require vast computational resources. The paper further delves into hybrid quantum-classical approaches, which leverage the strengths of both domains to address real-world problems. Challenges, such as quantum error correction, scalability, and the need for specialized hardware, are also discussed. We provide an analysis of ongoing advancements in quantum AI, including quantum-enhanced neural networks and reinforcement learning, and their implications for fields like natural language processing and predictive analytics. This research emphasizes the transformative potential of quantum AI while acknowledging the significant technical hurdles that remain. The integration of quantum computing and AI promises to unlock unprecedented computational capabilities, paving the way for breakthroughs in scientific research, industry applications, and complex problem-solving.