ebook9783030830984d4325

Description

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.