Titlebook: Concise Guide to Quantum Machine Learning; Davide Pastorello Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive li

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https://doi.org/10.1007/978-981-19-6897-6Quantum Computing; Quantum Neural Networks; Quantum Annealing; Machine Leaning; Quantum Algorithms

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978-981-19-6899-0The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor

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Machine Learning: Foundations, Methodologies, and Applicationshttp://image.papertrans.cn/c/image/235112.jpg

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María-Isabel Soldevila,Julie Vander Meulenssical networks, for instance a quantum feedforward neural network implemented in terms of a variational circuit is able to achieve a higher value of effective dimension (that is a figure of merit to quantify the generalization capability of a model) with respect to classical feedforward networks [A

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2730-9908 ics and linear algebra is sufficient; no previous experience with quantum computing or machine learning is needed. The book is aimed at researchers and students with no background in quantum physics and is also978-981-19-6899-0978-981-19-6897-6Series ISSN 2730-9908 Series E-ISSN 2730-9916