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Supervised Learning with Quantum Computers
Engleski
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This book investigates how quantum computers can be used for data-driven prediction. It summarizes and conceptualizes ideas that have been proposed in the discipline of quantum machine learning to provide a starting point for those new to the field, while serving as a reference for readers familiar with the topic. Given the interdisciplinary nature of the subject, the first chapters work through a simple but illustrative quantum machine learning algorithm and give a detailed overview of the parent disciplines. The book then presents core methods for the design of quantum machine learning algorithms with a focus on supervised learning. Amongst these methods are the representation of data by quantum states, quantum routines for inference and training, learning with quantum models, as well as near-term applications. The book contributes to research in quantum machine learning and targets an interdisciplinary audience of computer scientists and physicists from a graduate level onwards.
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