A Hybrid Quantum-Classical Algorithm for Underwater Target Classification

Offshore channel clearance is an essential underwater task to protect vessels and divers effectively, but current target classification relies heavily on operator identification. Machine learning provides highly accurate methods for image as well detection. In this paper, a new hybrid quantum-classical algorithm proposed. It uses quantum devices reduce dimension denoise data sets, greatly reducing the difficulty of classical computer processing data. Using abundant algorithms, problem different scenarios can be processed, improving efficiency. two kinds object sets examples, numerical simulation results show that accurately achieve dimensionality reduction. This has polynomial acceleration in reduction than methods, even considering readout The also accuracy training set improves from 0.772 0.821 compared original dataset. Furthermore, classifiers selected case objects, so broad application prospects fields.