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This paper presents a new dataset for fine-grained visual classification (FGVC) of fish species in their natural environment. It contains 794 images of 12 different fish species collected at the Adriatic sea in Croatia. All images show fishes in real live situations, recorded by high definition cameras. Remote and diver-based videography is used by a growing number of marine researchers to understand spatial and temporal variability of habitats and species. The required large numbers of independent observations necessitate the development of computer vision tools for an automated processing of high volumes of videos featuring high fish richness and density. As baseline experiment, we are using CNN features [1] and a linear SVM classifier and achieve an accuracy of 66.78% on our dataset.

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Machine learning is considered to be one of the most promising applications of quantum computing. Therefore, the search for quantum advantage of the quantum analogues of machine learning models is a key research goal. Here, we show that variational quantum classifiers and support vector machines with quantum kernels can solve a classification problem based on the k-FORRELATION problem, which is known to be PROMISEBQP-complete. Because the PROMISEBQP complexity class includes all Bounded-Error Quantum Polynomial-Time (BQP) decision problems, our results imply that there exists a feature map and a quantum kernel that make variational quantum classifiers and quantum kernel support vector machines efficient solvers for any BQP problem. Hence, this work implies that their feature map and quantum kernel, respectively, can be designed to have a quantum advantage for any classification problem that cannot be classically solved in polynomial time but contrariwise by a quantum computer.