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...
| Κύριοι συγγραφείς: | , |
|---|---|
| Συγγραφή απο Οργανισμό/Αρχή: | |
| Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
| Γλώσσα: | English |
| Έκδοση: |
Cham :
Springer International Publishing : Imprint: Springer,
2018.
|
| Έκδοση: | 1st ed. 2018. |
| Σειρά: | Quantum Science and Technology,
|
| Θέματα: | |
| Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- 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.
