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.
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.
The entwining of quantum computing with classical machine learning has proven to be one of the most promising frontiers of artificial intelligence, as it possesses the potential to speed up intricate computations in ways that classical systems will never be able to achieve. This study delves into the theoretical and practical implications of hybrid quantum-classical approaches in machine learning, while also examining their advantages, limitations, and applicability in different domains. Utilizing quantum entanglement and parallelism, hybrid solutions also have the potential to process high-dimensional data, optimize complex functions, and facilitate pattern recognition tasks more efficiently. However, noise, decoherence, and limited qubit connectivity on current hardware pose considerable barriers to practical implementations. The authors discuss hybrid architectures like quantum-enhanced optimizers and variational quantum circuits which include classical pre- and post-processing to alleviate quantum limitations. Moreover, a comparative study is performed to show the effectiveness of hybrid models comparing with classical machine learning state-of-the-art in natural language processing, computer vision and financial modeling. Results indicate that although hybrid quantum–classical approaches provide significant gains in optimization and computational efficiency; these benefits remain limited by hardware limitations and the scalability of quantum algorithms. With these considerations in mind, the paper offers guidance for targeting future research directions in quantum machine learning, including advances in quantum error correction, quantum hardware scalability, and new algorithmic approaches. Additional advancements needed and notably, the study focused on a smaller representation of data Since quantum computing is relatively new and researchers are still exploring its application, it is still early days in terms of machine learning models that are solely, intrinsically quantum.
Quantum Machine Learning represents a paradigm shift at the intersection of Quantum Computing and Machine Learning, leveraging quantum phenomena such as superposition, entanglement, and quantum parallelism to address the limitations of classical approaches in processing high-dimensional and large-scale datasets. This survey provides a comprehensive analysis of Quantum Machine Learning, detailing foundational concepts, algorithmic advancements, and their applications across domains such as healthcare, finance, and quantum chemistry. Key techniques, including Quantum Support Vector Machine, Quantum Neural Network, Quantum Decision Trees, and hybrid quantum-classical models, are explored with a focus on their theoretical foundations, computational benefits, and comparative performance against classical counterparts. While the potential for exponential speedups and enhanced efficiency is evident, the field faces significant challenges, including hardware constraints, noise, and limited qubit coherence in the current era of Noisy Intermediate-Scale Quantum devices. Emerging solutions, such as error mitigation techniques, hybrid frameworks, and advancements in quantum hardware, are discussed as critical enablers for scalable and fault-tolerant Quantum Machine Learning systems. By synthesizing state-of-the-art developments and identifying research gaps, this survey aims to provide a foundational resource for advancing Quantum Machine Learning toward practical, real-world applications in tackling computationally intensive problems.
Quantum Machine Learning (QML) promises the transformative potential in computer vision by utilizing quantum computing to facilitate faster high-dimensional data processing. In this paper, we will go through some of the recent works that employ QML for computer vision problems such as Image Segmentation, Classification, and Generation. Demonstrations aimed at showing where QML methods beat the state of art techniques in particular applications like facial recognition, medical imaging, and satellite imagery. QML aspires to make pathbreaking changes in a field limited by current hardware capabilities. This poster abstract summarizes the important studies, methodologies and findings to inform further research in this developing field.
The field of Quantum Artificial Intelligence (Quantum AI) has emerged as a promising interdisciplinary research area where the computational power of quantum computing is merged with the learning and decision-making power of AI. The application of quantum principles such as superposition, entanglement and quantum interference to machine learning algorithms has created new possibilities for solving problems that may be difficult to solve by classical machines. In the last few years, advances in Noisy Intermediate-Scale Quantum (NISQ) devices have helped to speed advances in Quantum Machine Learning (QML) algorithms, Quantum Neural Networks (QNNs), Quantum Support Vector Machines (QSVMs), Quantum Convolutional Neural Networks (QCNNs) and hybrid learning algorithms. This review provides a comprehensive overview of the research in the field of Quantum AI from 2020 to 2026, covering the key concepts, novel architectures, algorithmic advancements, and practical applications. A critical assessment of the application of Quantum AI in fields such as healthcare, finance, cybersecurity, scientific computing and optimization problems is provided, and the benefits of quantum-enhanced learning models are discussed. In addition, common difficulties such as quantum hardware limitations, sensitivity to noise, encoding of data, scalability constraints and a lack of speed in training variational quantum circuits are discussed. The review also covers new avenues of research, including trustworthy quantum machine learning, explainable Quantum AI, quantum federated learning, and fault-tolerant quantum computing. The results suggest while practical large-scale deployment is currently limited by existing hardware capabilities, hybrid quantum-classical systems have great potential to rapidly move intelligent data processing and develop next-generation AI systems.
The burgeoning field of machine learning has transformed numerous sectors, revolutionizing everything from image recognition to financial forecasting. However, classical machine learning algorithms often encounter limitations when dealing with complex, high-dimensional problems. This is where the nascent field of quantum machine learning (QML) emerges, offering a paradigm shift with its unique computational capabilities. By harnessing the principles of quantum mechanics, QML promises to solve problems intractable for classical methods, like simulating complex molecules or optimizing financial portfolios. However, current quantum hardware limitations necessitate a hybrid approach: Hybrid Quantum-Classical Machine Learning Models (HQCLML). The convergence of quantum computing and classical machine learning has sparked significant interest in the development of hybrid quantum-classical machine learning models. This research explores the synergy between quantum and classical paradigms, aiming to leverage the strengths of both to enhance the capabilities of machine learning algorithms. The paper provides an in-depth overview of quantum computing principles, classical machine learning models, and the foundational concepts that form the basis for hybrid models. Various approaches to integrating quantum computing into machine learning are discussed, emphasizing the potential advantages in solving complex problems, particularly those involving large-scale optimization or exponential search spaces. The study delves into quantum machine learning algorithms, showcasing examples such as Quantum Support Vector Machines and Quantum Neural Networks. Case studies and applications of hybrid models are presented to illustrate instances where quantum enhancements outperform classical counterparts. While highlighting the promising achievements, the paper also addresses the current challenges and limitations associated with hybrid models, including practical considerations, error rates, and the impact of decoherence in quantum computing. As quantum hardware technologies continue to advance, the paper explores the current landscape of quantum processors and their implications for hybrid models. The discussion extends to future directions, offering predictions for the development of hybrid quantum-classical machine learning models. Emerging technologies and potential breakthroughs are considered, presenting a forward-looking perspective on the evolving landscape of artificial intelligence research. In conclusion, the research underscores the significance of hybrid quantum-classical machine learning models as a transformative avenue for addressing complex computational problems. The synergy between quantum and classical approaches holds immense potential for advancing the field of machine learning, opening new horizons for solving problems that were once deemed computationally intractable.
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.