Autore |
Schuld Maria
|
Edizione | [1st ed. 2018.] |
Pubbl/distr/stampa |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018
|
Descrizione fisica |
1 online resource (293 pages)
|
Disciplina |
530.1201514
|
Collana |
Quantum Science and Technology
|
Soggetto topico |
Quantum physics
Quantum computers
Pattern recognition
Spintronics
Physics
Artificial intelligence
|
ISBN |
3-319-96424-0
|
Formato |
Materiale a stampa ![](img/format/mas.png) |
Livello bibliografico |
Monografia |
Lingua di pubblicazione |
eng
|
Nota di contenuto |
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 build quantum 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.
|
Record Nr. | UNINA-9910300533803321 |