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Encouraged by growing computing power and algorithmic development, machine learning technologies have became powerful tools for a wide variety of application areas, spanning from agriculture to chemistry and natural language processing. The use of quantum systems to process classical data using machine learning algorithms has given rise to an emerging research area, i.e. quantum machine learning. Despite its origins in the processing of classical data, quantum machine learning also explores the use of quantum phenomena for learning systems, the use of quantum computers for learning on quantum data and how machine learning algorithms and software can be formulated and implemented on quantum computers. Quantum machine learning can have a transformational effect in computer science. It may speed up the processing of information well beyond the existing classical speeds. Recent work has seen the development of quantum algorithms that could serve as foundations for machine learning applications. Despite its great promise, there are still significant hardware and software challenges that need to be resolved before quantum machine learning becomes practical. In this paper, we present an overview of quantum machine learning in the light of classical approaches. Departing from foundational concepts of machine learning and quantum computing, we discusses various technical contributions, strengths and similarities of research works in this domain. We also elaborate upon recent progress of different quantum machine learning approaches, their complexity and applications in various fields such as physics, chemistry and natural language processing.

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Devising automated procedures for accurate vessel segmentation (retinal) is crucial for timely prognosis of vision-threatening eye diseases. In this paper, a novel supervised deep learning-based approach is proposed which extends a variant of the fully convolutional neural network. The existing fully convolutional neural network-based counterparts have associated critical drawbacks of involving a large number of tunable hyper-parameters and an increased end-toend training time furnished by their decoder structure. The proposed approach addresses these intricate challenges by using a skip-connections strategy by sharing indices obtained through maxpooling to the decoder from the encoder stage (respective stages) for enhancing the resolution of the feature map. This significantly reduces the number of required tunable hyper-parameters and the computational overhead of the training as well as testing stages. Furthermore, the proposed approach particularly helps in eradicating the requirement for employing both postprocessing and pre-processing steps. In the proposed approach, the retinal vessel segmentation problem is formulated as a semantic pixel-wise segmentation task which helps in spanning the gap between semantic segmentation and medical image segmentation. A prime contribution of the proposed approach is the introduction of external skip-connection for passing the preserved lowlevel semantic edge information in order to reliably detect tiny vessels in the retinal fundus images. The performance of the proposed scheme is analyzed based on the three publicly available notable fundus image datasets, while the widely recognized evaluation metrics of specificity, sensitivity, accuracy, and the Receiver Operating Characteristics curves are used. Based on the assessment of the images in {DRIVE, CHASE_DB1, and STARE} datasets, the proposed approach achieves a sensitivity, specificity, accuracy, and ROC performance of {0.