Supervised Learning with Quantum Computers - Petruccione, Francesco

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Résumé :
Introduction.- Background.- How quantum computers can classify data.- Organisation of the book.-? Machine Learning.- Prediction.- Models.- Training.- Methods in machine learning.- Quantum Information.- Introduction to quantum theory.- Introduction to quantum computing.- An example: The Deutsch-Josza algorithm.- Strategies of information encoding.- Important quantum routines.- Quantum advantages.- Computational complexity of learning.- Sample complexity.- Model complexity.- Information encoding.-? Basis encoding.- Amplitude encoding.- Qsample encoding.- Hamiltonian encoding.- Quantum computing for inference.- Linear models.- Kernel methods.- Probabilistic models.- Quantum computing for training.-? Quantum blas.- Search and amplitude amplification.- Hybrid training for variational algorithms.- Quantum adiabatic machine learning.- Learning with quantum models.- Quantum extensions of Ising-type models.- Variational classifiers and neural networks.- Other approaches to buildquantum models.- Prospects for near-term quantum machine learning.- Small versus big data.- Hybrid versus fully coherent approaches.- Qualitative versus quantitative advantages.- What machine learning can do for quantum computing.- References.

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Biographie:
Maria Schuld works as a researcher for the Toronto-based quantum computing start-up Xanadu. She received her Ph.D. from the University of KwaZulu-Natal in 2017, where she began working on the intersection between quantum computing and machine learning in 2013. Besides her numerous contributions to the field, she is a co-developer for the open-source quantum machine learning software framework PennyLane. Francesco Petruccione received his Ph.D. (1988) and Habilitation (1994) from the University of Freiburg, Germany. Since 2004, he has been a professor of Theoretical Physics at the University of KwaZulu-Natal in Durban, South Africa, where in 2007, he was granted a South African Research Chair for Quantum Information Processing and Communication. He is the co-author of The Theory of Open Quantum Systems (Oxford University Press, 2002) and has published more than 250 papers in refereed journals. Francesco Petruccione's research focuses on open quantum systems and quantum information processing and communication....

Sommaire:
Introduction.- Background.- How quantum computers can classify data.- Organisation of the book.-? Machine Learning.- Prediction.- Models.- Training.- Methods in machine learning.- Quantum Information.- Introduction to quantum theory.- Introduction to quantum computing.- An example: The Deutsch-Josza algorithm.- Strategies of information encoding.- Important quantum routines.- Quantum advantages.- Computational complexity of learning.- Sample complexity.- Model complexity.- Information encoding.-? Basis encoding.- Amplitude encoding.- Qsample encoding.- Hamiltonian encoding.- Quantum computing for inference.- Linear models.- Kernel methods.- Probabilistic models.- Quantum computing for training.-? Quantum blas.- Search and amplitude amplification.- Hybrid training for variational algorithms.- Quantum adiabatic machine learning.- Learning with quantum models.- Quantum extensions of Ising-type models.- Variational classifiers and neural networks.- Other approaches to buildquantum models.- Prospects for near-term quantum machine learning.- Small versus big data.- Hybrid versus fully coherent approaches.- Qualitative versus quantitative advantages.- What machine learning can do for quantum computing.- References.

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