This book offers an introduction into quantum machine learning research, covering approaches that range from "near-term" to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from data. Among the topics discussed are parameterized quantum circuits, hybrid optimization, data encoding, quantum feature maps and kernel methods, quantum learning theory, as well as quantum neural networks. The book aims at an audience of computer scientists and physicists at the graduate level onwards. The second edition extends the material beyond supervised learning and puts a special focus on the developments in near-term quantum machine learning seen over the past few years.
Procurando Machine Learning with Quantum Computers? Aqui você encontra tudo sobre este livro de Maria Schuld, publicado por Springer em 18 de outubro de 2021. Nesta página estão a descrição da obra, os detalhes da edição (312 páginas) e os formatos disponíveis para baixar: pdf, epub, txt, djvu. Se você gosta de Livros Internacionais, Ciências Tecnológicas, Matemática, Aplicada, Probabilidade e Estatística, explore também outros títulos da mesma categoria no Encontrando os melhores livros. Veja ainda as outras obras de Maria Schuld em nosso catálogo.
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