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Machine Learning in Quantum Sciences

Machine Learning in Quantum Sciences

Machine Learning in Quantum Sciences
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Machine Learning in Quantum Sciences Hardback - 2025

by Anna Dawid , Julian Arnold , Borja Requena , Alexander Gresch , Marcin Płodzień , Kaelan Donatella , Kim A. Nicoli , Paolo Stornati , Rouven Koch , Miriam Büttner , Robert Okuła , Gorka Muñoz-Gil , Rodrigo A. Vargas-Hernández , Alba C

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Cambridge University Press, 2025. Hardcover. New. 330 pages. 7.00x0.75x10.00 inches.
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Details

  • Title Machine Learning in Quantum Sciences
  • Author Anna Dawid , Julian Arnold , Borja Requena , Alexander Gresch , Marcin PÅ‚odzieÅ„ , Kaelan Donatella , Kim A. Nicoli , Paolo Stornati , Rouven Koch , Miriam Büttner , Robert OkuÅ‚a , Gorka Muñoz-Gil , Rodrigo A. Vargas-Hernández , Alba C
  • Binding Hardback
  • Condition New
  • Pages 330
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press
  • Publication date 2025
  • Bookseller's Inventory # x-1009504932
  • ISBN 9781009504935 / 1009504932
  • Weight 1.74 lbs (0.79 kg)
  • Dimensions 10 x 7 x 0.75 in (25.40 x 17.78 x 1.91 cm)
  • Category Science
  • Quantity available 2

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From the publisher

Artificial intelligence is dramatically reshaping scientific research and is coming to play an essential role in scientific and technological development by enhancing and accelerating discovery across multiple fields. This book dives into the interplay between artificial intelligence and the quantum sciences; the outcome of a collaborative effort from world-leading experts. After presenting the key concepts and foundations of machine learning, a subfield of artificial intelligence, its applications in quantum chemistry and physics are presented in an accessible way, enabling readers to engage with emerging literature on machine learning in science. By examining its state-of-the-art applications, readers will discover how machine learning is being applied within their own field and appreciate its broader impact on science and technology. This book is accessible to undergraduates and more advanced readers from physics, chemistry, engineering, and computer science. Online resources include Jupyter notebooks to expand and develop upon key topics introduced in the book.

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