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.
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.
Quantum Machine Learning (QML) is an interdisciplinary field combining Quantum Computing (QC) and Machine Learning (ML). It has gained increased attention due to advances in near-term hardware implementations of quantum devices. The use of QML has proven to result in a significant improvement in performance and computational speed. Consequently, QML has become an effective technique for data processing and classification. Researchers have recently proposed various QML solutions in the medical image analysis field to gain an advantage of quantum supremacy. The main objective of this work is to present a holistic review of current leading-edge published works in the quantum medical image analysis field with a focus on supervised learning using artificial neural networks. A comparative study is used to pinpoint the potential of existing techniques, the most promising techniques, and the future of research in this area.
This research delves into quantum machine learning (QML) in the context of computer vision analysis by exploring the progress made in quantum computing and its impact on machine learning applications such as managing datasets and improving large-scale data processing efficiency through QML techniques specialised for tasks like image segmentation and classification in computer vision projects, along with findings, from trials conducted using the EMNIST benchmark dataset.Our tests reached an accuracy level above 90% successfully categorising tasks, with precision. This study explores the uses of quantum machine learning (QML) in areas like identification medical scans and distant monitoring. It also delves into the existing constraints and hurdles linked to quantum computer technologies.
The combination of Quantum Computing with Machine Learning approaches is refered as Quantum Machine Learning (QML). Recently, there have been significant advancements in performance and computational speed to quantum computing techniques. Although traditional algorithms have shown promising outcomes, their low processing efficiency and limited labelled data are common problems. To address these issues, a great deal of research has brought machine learning and quantum computing together, which has led to the development of increasingly complex algorithms. Notable gains in parameter optimisation, execution efficiency, and error rate reduction have resulted from these developments. The incorporation of quantum machine learning has yielded novel perspectives into the transdisciplinary domain of quantum technology and medical image analysis, indicating noteworthy advancements to be made in this area going forward. QML is becoming known for its unique superposition and entanglement capabilities, which make it a cutting-edge approach to challenges involving categorization. Its uses in fields such as pattern identification, password cracking, and medical image analysis have attracted a lot of attention. Because of this, a growing number of academics are using QML-based algorithms to analyse medical data. This research review's key goal is to present an in-depth analysis of how QML techniques are used and involved in the medical field. This article includes a synopsis of the description and classification of quantum machine learning in addition to a rundown of the many QML techniques and how they have been used in medical image investigation during the previous years.
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.
Quantum Machine Learning (QML) and Quantum Deep Learning (QDL) are emerging research areas that combine quantum computing with machine learning and deep learning techniques.These approaches aim to improve computational speed, feature representation, optimisation, and learning performance in complex data-driven problems.This review paper presents a structured discussion of recent developments in QML and QDL, focusing on major algorithms, application areas, implementation approaches, current limitations, and future research directions.The review covers important quantum-based learning methods, including quantum support vector machines, quantum k-nearest neighbours, quantum principal component analysis, quantum neural networks, variational quantum classifiers, quantum convolutional neural networks, quantum reinforcement learning, quantum generative models, and hybrid quantum-classical models.The paper also discusses how these methods are being explored in healthcare, finance, cybersecurity, communication networks, image processing, natural language processing, and optimisation.However, the review shows that most QML and QDL models are still in the early stages of research.Current challenges include noisy quantum hardware, limited qubit count, data encoding issues, circuit design problems, difficulties with model training, and a lack of standardised benchmarks.The review concludes that QML and QDL have strong future potential, but their practical success depends on improved hardware, stronger algorithms, reliable frameworks, and fair comparison with classical models.
Quantum Machine Learning (QML) is an emerging, powerful paradigm at the intersection of quantum computing and artificial intelligence, with the potential to enhance medical image analysis. We compared their performance with classical counterparts, while critically examining the gap between theoretical quantum advantages and the practical constraints of current Noisy Intermediate-Scale Quantum (NISQ) implementations. This review explored the current landscape of QML-based methodologies applied to key medical imaging modalities - Magnetic Resonance Imaging (MRI), Computed Tomography (CT), X-rays, and histopathology. We systematically analyzed how quantum-enhanced models were employed for critical tasks such as image classification, segmentation, and reconstruction, and we compared their performance with that of classical counterparts. The review delved into the architectural innovations that enabled these quantum approaches, including hybrid quantum-classical frameworks, variational quantum circuits, and quantum convolutional neural networks. Particular emphasis was given to the clinical relevance of QML applications, from early diagnostics to real-time surgery planning, and how quantum advantage might influence data efficiency, interpretability, along computational scalability. Finally, we highlighted current challenges in data encoding, noise resilience, and hardware limitations, and discussed promising future directions that could enable the practical deployment of QML in healthcare settings. This comprehensive review aimed to serve as a foundational reference for researchers and practitioners looking to harness quantum computing in medical imaging and precision medicine.