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Supervised learning with quantum computers

By: Contributor(s): Language: English Series: Quantum science and technology | / edited by Raymond LaflammePublication details: Springer 2018 SwitzerlandDescription: xiii, 287pISBN:
  • 9783319964232
Subject(s): DDC classification:
  • 006.31 Sch77s
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.
List(s) this item appears in: New arrival September 16 to 22, 2019
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Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
Books Books PK Kelkar Library, IIT Kanpur General Stacks 006.31 Sch77s (Browse shelf(Opens below)) Available A184797
Total holds: 0

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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