Descript 
1 online resource (xiii, 287 pages) : illustrations, digital ; 24 cm 

text txt rdacontent 

computer c rdamedia 

online resource cr rdacarrier 

text file PDF rda 
Series 
Quantum science and technology, 23649054


Quantum science and technology

Note 
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 DeutschJosza 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 Isingtype models  Variational classifiers and neural networks  Other approaches to build quantum models  Prospects for nearterm 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 

Quantum machine learning investigates how quantum computers can be used for datadriven 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 nearterm quantum devices 
Host Item 
Springer eBooks

Subject 
Quantum theory


Machine learning


Quantum Physics


Quantum Computing


Pattern Recognition


Quantum Information Technology, Spintronics


Numerical and Computational Physics, Simulation


Artificial Intelligence (incl. Robotics)

Alt Author 
Petruccione, Francesco, author


SpringerLink (Online service)

