Supervised learning with quantum computers
Language: English Series: Quantum science and technology | / edited by Raymond LaflammePublication details: Springer 2018 SwitzerlandDescription: xiii, 287pISBN:- 9783319964232
- 006.31 Sch77s
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PK Kelkar Library, IIT Kanpur | General Stacks | 006.31 Sch77s (Browse shelf(Opens below)) | Available | A184797 |
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006.31 Sa71i Information extraction | 006.31 Sca45 Scaling up machine learning | 006.31 SCH64L LEARNING WITH KERNELS | 006.31 Sch77s Supervised learning with quantum computers | 006.31 St37s Support vector machines | 006.31 Su35i Introduction to statistical machine learning | 006.31 Su35m Machine learning in non-stationary environments |
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.
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