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Supervised learning with quantum computers

By: Schuld, Maria.
Series: Quantum science and technology.Publisher: Cham Springer 2018Description: xiii, 287 p. ill. 24 cm.ISBN: 9783319964232.Subject(s): Pattern recognition | Quantum physics | Mathematical physics | Artificial intelligence | Physics | Computers | Computer vision and pattern recognition | Information technology | Intelligence and​ semantics | Mathematical theory of computation | Quantum theory | Machine learning | Data processing | Optical pattern recognition | Quantum computing | Quantum information technology, spintronics | Numerical and computational physics, simulationDDC classification: 530.1201514 Summary: 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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Books 530.1201514 SCH (Browse shelf) Checked out 15/05/2024 032104

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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