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.
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.
The emergence of deep vision models such as convolutional neural networks and vision transformers has revolutionized computer vision, enabling significant advancements in image classification, object detection, and segmentation. In parallel, the rapid development of quantum computing has spurred interest in quantum machine learning (QML), which integrates the strengths of quantum computation with the representational power of deep learning. In QML, parameterized quantum circuits offer the potential to capture complex image features, define complex decision boundaries, and provide other computational advantages. This paper investigates hybrid quantum-classical vision architectures, with a focus on hybrid quantum-classical convolutional neural networks and hybrid quantum-classical vision transformers. These hybrid models explore both quantum pre-processing and post-processing of data, respectively, where quantum circuits are strategically integrated into the data pipeline to enhance model performance. Our results suggest that these hybrid models can enhance accuracy and computational efficiency in vision-related tasks, even with the constraints of current noisy intermediate-scale quantum devices.
Machine learning (ML) has proven its efficacy in acquiring information from previous data and lever-aging that understanding to handle practical problems. The recent development of quantum computing, based on the principles of quantum physics, has made the integration of quantum computing and machine learning a prominent area of research. Nonetheless, the practical utilization of quantum computing in machine learning continues to present considerable obstacles, especially regarding implementation and the management of real-world tasks. This research presents a hybrid quantum-classical convolutional neural network model (HQ-CNN) and evaluates its performance on binary image classification tasks using subsets of the MNIST and EMNIST datasets. The study also presents an in-depth discussion of the challenges that arise from integrating quantum computing into machine learning, emphasizing the training procedure, the results obtained post-training, and the number of qubits used in the research. The experimental findings indicate that increasing the number of qubits generally enhances image classification efficacy. However, an increased number of qubits does not inherently ensure enhanced overall model performance.
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.
Machine learning (ML) has proven its efficacy in acquiring information from previous data and lever-aging that understanding to handle practical problems. The recent development of quantum computing, based on the principles of quantum physics, has made the integration of quantum computing and machine learning a prominent area of research. Nonetheless, the practical utilization of quantum computing in machine learning continues to present considerable obstacles, especially regarding implementation and the management of real-world tasks. This research presents a hybrid quantum-classical convolutional neural network model (HQ-CNN) and evaluates its performance on binary image classification tasks using subsets of the MNIST and EMNIST datasets. The study also presents an in-depth discussion of the challenges that arise from integrating quantum computing into machine learning, emphasizing the training procedure, the results obtained post-training, and the number of qubits used in the research. The experimental findings indicate that increasing the number of qubits generally enhances image classification efficacy. However, an increased number of qubits does not inherently ensure enhanced overall model performance.
Quantum Computing (QC) was born as a purely theoretical subject. Thus, in the beginning, the main category of researchers in the area was physicists and mathematicians. Since IBM introduced the first quantum computer, global interest in this emerging technology has been growing more and more, involving computer science and engineering researchers. Machine learning is another rapidly growing technology, used in a wide range of applications where traditional computing is not sufficient. The two technologies are merged in the thesis work. The different ways in which a combination of them can occur are discussed, as well as the explanation why there is not a single definition of Quantum Machine Learning (QML). Firstly, quantum computing is examined in depth. The basic elements of quantum computation theory, based on the fundamental principles of quantum mechanics, are introduced. The attention is focused on the qubit description on the Bloch sphere, the fundamental quantum gates, and examples of quantum algorithms. Therefore, the differences between quantum simulators and quantum processors are analysed, including the frameworks that provide tools to interact with them. Focusing on Quantum Machine Learning, the different definitions of the technology are described. There are four distinct approaches that are differentiated by the type of data used (quantum or classical) and by the device processing them (quantum or classical). In this thesis, QML is defined as the approach that uses classical data to feed a quantum device. The two possible methods for developing QML algorithms are: 1) to translate existing classical models into the terms of quantum computing; 2) to leave the existing models of classical machine learning completely, and explore new possible ways of doing QML to take full advantage of the physical characteristics of a quantum computer. The strategy adopted is the first one, thus it is used a state-of-the-art hybrid model of Quanvolutional Neural Network (QNN), composed of both quanvolutional and classical convolutional layers. The quanvolutional layers reproduce the same concept of the classical ones: they are made of quantum filters that generate feature maps through locally transforming input data. The transformation can be performed by variational quantum circuits or random quantum circuits. The specific field where Quantum Machine Learning is applied is image classification. The considered case study is a labeled dataset with X-ray images of lungs (labels: pneumonia, normal). The images are pre-processed and cropped for being used by quantum circuits, thus some information is lost. The main aim of the thesis is to understand whether and in which ways Quantum Machine Learning could bring advantages in the mentioned classification in terms of timing, accuracy, or dataset reduction, despite the large decrease in images' resolution. Different configurations of the hybrid model (with a different number of quantum filters and different settings of convolutional layers) are analysed and tested. The current quantum devices have many limitations, thus the time is still immature to observe a significant change in perspective. Nevertheless, the obtained results are promising for the future of QML in terms of dataset and hyperparameters reduction.
Abstract Image classification, a pivotal task in multiple industries, faces computational challenges due to the burgeoning volume of visual data. This research addresses these challenges by introducing two quantum machine learning models that leverage the principles of quantum mechanics for effective computations. Our first model, a hybrid quantum neural network with parallel quantum circuits, enables the execution of computations even in the noisy intermediate-scale quantum era, where circuits with a large number of qubits are currently infeasible. This model demonstrated a record-breaking classification accuracy of 99.21% on the full MNIST dataset, surpassing the performance of known quantum–classical models, while having eight times fewer parameters than its classical counterpart. Also, the results of testing this hybrid model on a Medical MNIST (classification accuracy over 99%), and on CIFAR-10 (classification accuracy over 82%), can serve as evidence of the generalizability of the model and highlights the efficiency of quantum layers in distinguishing common features of input data. Our second model introduces a hybrid quantum neural network with a Quanvolutional layer, reducing image resolution via a convolution process. The model matches the performance of its classical counterpart, having four times fewer trainable parameters, and outperforms a classical model with equal weight parameters. These models represent advancements in quantum machine learning research and illuminate the path towards more accurate image classification systems.