Title Machine Learning with Quantum Computers Author(s) Schuld, Maria;Petruccione, Francesco Publication Cham, Springer International Publishing, 2021. Description XIV, 312 p. 104 illus., 74 illus. in color : online resource Abstract Note 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 ISBN,Price 9783030830984 Keyword(s) 1. EBOOK 2. EBOOK - SPRINGER 3. MACHINE LEARNING 4. MATHEMATICS 5. Mathematics and Computing 6. QUANTUM COMPUTERS 7. Quantum computing Item Type eBook Multi-Media Links Please Click here for eBook Circulation Data Accession#   Call# Status Issued To Return Due On Physical Location I11858     On Shelf