Supervised Learning with Quantum Computers
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Beschreibung
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.
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.
Explains relevant concepts and terminology from machine learning and quantum information in an accessible language Introduces a structure into the literature by clustering the work in terms of what aspects of quantum information are exploited to advance machine learning Critically reviews challenges that are a common theme in the works Gives a comprehensive outlook on future directions
Autor*in
Maria Schuld
Themen in »Supervised Learning with Quantum Computers«
quantum phase estimation quantum walks quantum annealing hidden Markov models belief nets Boltzmann machines adiabatic quantum computing Grover search Hopfield models Quantum inference Artificial neural network near term application Quantum machine learning data driven prediction Qsample encoding
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“The book is very well written and contains sufficiently many examples and illustrations. The authors make a concerted effort to make the material accessible to both computer science graduates as well as scientists with a quantum physics background. … The intended audience are thus machine learning scientists that want to explore the quantum approach to their discipline or quantum information scientists that want to enter the field of machine learning.” (Andreas Maletti, zbMATH 1411.81008, 2019)
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