The emergence of Big Data changed how we approach information systems engineering. Nowadays, when we can use remote sensing techniques for Big Data acquisition, the issues such data introduce are as important as ever. One of those concerns is the processing of the data. Classical methods often fail to address that problem or are incapable of processing the data in a reasonable time. With that in mind information system engineers are required to investigate different approaches to the data processing. The recent advancements in noisy intermediate-scale quantum (NISQ) devices implementation allow us to investigate their application to real-life computational problem. This field of study is called quantum (information) systems engineering and usually focuses on technical problems with the contemporary devices. However, hardware challenges are not the only ones that hinder our quantum computation capabilities. Software limitations are the other, less explored side of this medal. Using multispectral image segmentation as a task example, we investigated how difficult it is to run a hybrid quantum-classical model on a real, publicly available quantum device. To quantify how and explain why the performance of our model changed when ran on a real device, we propose new explainability metrics. These metrics introduce new meaning to the explainable quantum machine learning; the explanation of the performance issue comes from the quantum device behavior. We also analyzed the expected money costs of running similar experiment on contemporary quantum devices using standard market prices.
The emergence of Big Data changed how we approach information systems engineering. Nowadays, when we can use remote sensing techniques for Big Data acquisition, the issues such data introduce are as important as ever. One of those concerns is the processing of the data. Classical methods often fail to address that problem or are incapable of processing the data in a reasonable time. With that in mind information system engineers are required to investigate different approaches to the data processing. The recent advancements in noisy intermediate-scale quantum (NISQ) devices implementation allow us to investigate their application to real-life computational problem. This field of study is called quantum (information) systems engineering and usually focuses on technical problems with the contemporary devices. However, hardware challenges are not the only ones that hinder our quantum computation capabilities. Software limitations are the other, less explored side of this medal. Using multispectral image segmentation as a task example, we investigated how difficult it is to run a hybrid quantum-classical model on a real, publicly available quantum device. To quantify how and explain why the performance of our model changed when ran on a real device, we propose new explainability metrics. These metrics introduce new meaning to the explainable quantum machine learning; the explanation of the performance issue comes from the quantum device behavior. We also analyzed the expected money costs of running similar experiment on contemporary quantum devices using standard market prices.
Abstract As machine learning grows increasingly complex due to big data and deep learning, model explainability has become essential to fostering user trust. Quantum machine learning (QML) has emerged as a promising field, leveraging quantum computing to enhance classical machine learning methods, particularly through quantum representation learning (QRL). QRL aims to provide more efficient and powerful machine learning capabilities on noisy intermediate-scale quantum (NISQ) devices. However, interpreting QRL models poses significant challenges due to the reliance on quantum gate-based parameterized circuits, which, while analogous to classical neural network layers, operate in the quantum domain. To address these challenges, we propose an explainable QRL framework combining a quantum autoencoder (QAE) with a variational quantum classifier (VQC) and incorporating theoretical and empirical explainability for image data. Our dual approach enhances model interpretability by integrating visual explanations via local interpretable model-agnostic explanations (LIME) and analytical insights using Shapley Additive Explanations (SHAP). These complementary methods provide a deeper understanding of the model’s decision-making process based on prediction outcomes. Experimental evaluations on simulators and superconducting quantum hardware validate the effectiveness of the proposed framework for classification tasks, underscoring the importance of explainable representation learning in advancing QML towards more transparent and reliable applications.
Abstract Image segmentation remains one of the most extensively studied problems in computer vision, with applications ranging from medical image analysis and autonomous driving to remote sensing and industrial inspection. While deep learning approaches currently dominate the field, interest in quantum computing has motivated the development of quantum and quantum-inspired segmentation techniques that explore alternative strategies for image representation, optimization, and learning. This survey provides a comprehensive review of quantum and quantum-inspired image segmentation methods within the broader context of modern segmentation research. To establish this context, we first examine the evolution of image segmentation methodologies, covering classical image processing techniques, machine learning approaches, and contemporary deep learning architectures.We then organize the existing quantum image segmentation literature into four categories: quantum-inspired, tensor network-based, hybrid quantum-classical, and fully quantum methods. Across these categories, we analyze computational principles, image encoding and representation strategies, application domains, and datasets. We further compare quantum and quantum-inspired approaches with established segmentation paradigms in terms of supervision, scalability, and hardware requirements. Our analysis indicates that quantum-inspired methods currently represent the most developed direction, while hybrid approaches are emerging and fully quantum methods remain constrained by quantum hardware, data encoding costs, and scalability challenges. By connecting quantum image segmentation with the broader evolution of segmentation research, this survey provides a unified perspective on the field. It identifies current trends, open challenges, and promising future directions for researchers exploring the emerging role of quantum computing in image segmentation.
Quantum machine learning (QML) provides a transformative approach to data analysis by integrating the principles of quantum computing with classical machine learning methods. With the exponential growth of data and the increasing complexity of computational tasks, quantum algorithms offer tremendous advantages in terms of processing speed, memory efficiency, and the ability to resolve issues intractable for classical systems. In this work, the use of QML techniques for both supervised and unsupervised learning problems is explored. Quantum-enhanced models such Quantum Support Vector Machines (QSVMs) and Quantum Neural Networks (QNNs) show outstanding performance in classification and regression tasks by using quantum kernels and entanglement in supervised learning. Moreover, hybrid quantum-classical solutions offer useful implementations on noisy intermediate-scale quantum (NISQ) devices, hence bridging the gap between present quantum technology and practical uses. By means of comparative analysis, this paper emphasizes the possible benefits and drawbacks of QML, thereby providing understanding of its future importance in sectors including material science, finance, and healthcare. In the end, QML opens the path for a new era of intelligent data processing and solves until unthinkable difficult challenges.
Enormous activity in the Quantum Computing area has resulted in it being considered, together with classical computers, for solving different difficult problems - including those of applied nature. An attempt is made in this work to assemble a pipeline consisting of both quantum and classical processing blocks for the task of image classification and segmentation, keeping in mind the present limitations of the gate-model quantum computers. It is based on the work done for the recent BMW Quantum Computing Challenge related to Automotive Industry. The pipeline handles the real-life sized images as the input and output, rather than the toy-sized examples prevalent in Quantum Computing literature. Apart from breaking down the problem to modules, some of which can be accommodated in the existing simulators and hardware, simplifications of the relevant quantum algorithms are also carried out. Its functionality and utility are brought out by applying it to surface crack segmentation on the popular Kaggle Surface Crack Detection data set. The results of the paper are not only limited to simulations, but also involve running models on Noisy, Intermediate-Scale Quantum processors through AWS. In its entirety, this work may lay the groundwork for quantum/quantum-enhanced image segmentation, and providing interested researchers with a stepping-stone in that direction, as the results demonstrate the efficacy of the proposed method, even with simple versions of the quantum modules.
Quantum computing has emerged as a transformative paradigm capable of addressing computational challenges beyond the reach of classical systems.In the current era of Noisy Intermediate-Scale Quantum (NISQ) devices, practical quantum hardware is characterized by limited qubit counts, short coherence times, gate errors, and the absence of full error correction.Despite these constraints, significant progress has been made in developing quantum machine learning (QML) techniques tailored to operate on near-term quantum processors.This paper presents a comprehensive review of Quantum Machine Learning in the context of NISQ computing.We first outline the fundamental principles of quantum computing, including qubits, superposition, entanglement, and quantum parallelism, followed by a detailed discussion of the NISQ computing model and its hardware limitations.The review then examines hybrid quantum-classical learning frameworks, variational quantum algorithms, quantum data encoding strategies, and noise mitigation techniques that enable practical implementation on existing platforms.Emerging applications across healthcare, finance, cybersecurity, climate modeling, and materials science are also surveyed to highlight the real-world potential of QML.Furthermore, key challenges such as scalability, trainability issues, and algorithmic robustness are analyzed, along with current research efforts aimed at overcoming these barriers.The paper concludes by identifying promising future directions for advancing QML toward quantum advantage.This review aims to provide researchers and practitioners with a structured understanding of the capabilities, limitations, and opportunities of quantum machine learning in the NISQ era .
The intersection of machine learning and quantum computing has been one of the greatest scientific projects in current time. With quantum computing in the offing, the assurance of using quantum mechanical effects in computational learning to transform the world has never been closer. The publication of this book comes at a critical time, when theoretical models are shouldering maturity with capabilities of experimentation, that has brought about great possibilities to implement computational benefits that quantum systems can provide in performing machine learning tasks. Quantum machine learning is one field which has rapidly changed in the last ten years. What was originally viewed as mere theoretical conjecture has now evolved into a living research community which includes rigorous mathematical modeling, novel algorithm design, and more and more complex experimental realisations on the near-term quantum computers. Such change can be characterised by the increasing sophistication of our theory in as well as the revolutionary advances in quantum computing technology, and by the noisy intermediate-scale quantum (NISQ) era. Chapters in this book chronologically discuss the complex nature of quantum machine learning, including theoretical principles and challenges of practice. All the chapters cover a very important aspect in this growing area leaving the reader not only with an in-depth analysis but also a wide enough point of view to traverse this somewhat complicated inter-disciplinary area. The book is organized in such a way that that it takes the reader through more advanced parts of the quantum machine learning theory and practice. We start by explaining quantum feature maps, and kernel-type methods and laying the mathematical background on which quantum-enhanced learning is based. There we discuss computational complexity, trainability issues, neural network architectures, scaling of resources, sample complexity limits and the issue critical of quantum data encoding. This book has a variety of audience. The works will also offer exhaustive proposals of key concepts, methodologies and open problems to graduate students and researchers venturing into the field. Individuals who operate quantum computing platforms will have an understanding of the options available when it comes to designing an algorithm and the resources needed, as well as the expectations of its performance. The systematic approach of tackling complexity theorist-foundations and rigorous mathematical analyses will be appreciated by the theorists. Quantum machine learning is interdisciplinary and requires the services and knowledge of quantum physics, computer science, mathematics, and statistics. Although we do not expect our readers to have the basic knowledge of quantum computing and classical machine learning, we give adequate background and references to accommodate other readers of varying technical background. By 2026, quantum machine learning will be on a important crossroads. NISQ has also provided its own stunning demonstrations and harsh lessons on the difficulty of obtaining useful quantum advantage. Other phenomena like barren plateaus have changed the minds of us on the nature of variational quantum algorithm trainability. Resource-efficient quantum learning has been transformed by the advanced measurement protocols, such as classical shadow tomography. The frontier of the implementable quantum algorithms is constantly being pushed by hardware advances. The book reflects the picture of the field in this dynamic time, including the latest theoretical developments, their experiments, and new optimal practices. We admit that quantum machine learning is a fast-developing science, and there are certain spheres of our knowledge that will surely become more profound in the nearest future. The transformative potential of quantum machine learning is still achievable only with further achievements in many dimensions: the theory of when and why quantum algorithms are effective, algorithmic innovations to overcome the limitations of near-term hardware, hardware engineering to overcome qubit quality and system scale, and discovering applications when quantum algorithms and methods and methods offer significant superior practical performance. Hopefully, this book will provide useful guidance to all the existing knowledge and guide to the future innovations. The opportunities are out of the world, yet the challenges are significant. With the ongoing development of quantum hardware capabilities and an ever-increasing theoretical basis, it is quite possible that quantum machine learning will transform the world of approaching the problem of complex computational learning.