Quantum machine learning investigates how quantum computers can be used for data-driven prediction and decision making. The books summarises and conceptualises ideas of this relatively young discipline for an audience of computer scientists and physicists from a graduate level upwards. It aims at providing a starting point for those new to the field, showcasing a toy example of a quantum machine learning algorithm and providing a detailed introduction of the two parent disciplines. For more advanced readers, the book discusses topics such as data encoding into quantum states, quantum algorithms and routines for inference and optimisation, as well as the construction and analysis of genuine ``quantum learning models''. A special focus lies on supervised learning, and applications for near-term quantum devices.
ISBN: | 9783319964232 |
Publication date: | 22nd September 2018 |
Author: | Maria Schuld, Francesco Petruccione |
Publisher: | Springer an imprint of Springer International Publishing |
Format: | Hardback |
Pagination: | 287 pages |
Series: | Quantum Science and Technology |
Genres: |
Quantum physics (quantum mechanics and quantum field theory) Condensed matter physics (liquid state and solid state physics) Pattern recognition Mathematical physics Materials science Mathematical theory of computation Artificial intelligence |