Supervised Learning with Quantum Computers
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 pr...
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| Hlavný autor: | |
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| Médium: | Elektronický zdroj E-kniha |
| Jazyk: | English |
| Vydavateľské údaje: |
Cham :
Springer International Publishing,
2018.
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| Vydanie: | 1st ed. 2018. |
| Edícia: | Quantum Science and Technology,
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| Predmet: | |
| ISBN: | 9783319964249 |
| ISSN: | 2364-9054 |
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Obsah:
- 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.