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Quantum Artificial Intelligence with Qiskit
Andreas Wichert
- 334 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Quantum Artificial Intelligence with Qiskit
Andreas Wichert
About This Book
Quantum Artificial Intelligence (QAI) is a new interdisciplinary research field that combines quantum computing with Artificial Intelligence (AI), aiming to use the unique properties of quantum computers to enhance the capabilities of AI systems. Quantum Artificial Intelligence with Qiskit provides a cohesive overview of the field of QAI, providing the tools for readers to create and manipulate quantum programs on devices as accessible as a laptop computer.
Introducing symbolical quantum algorithms, sub-symbolical quantum algorithms, and quantum Machine Learning (ML) algorithms, this book explains each process step by step with associated Qiskit listings. All examples are additionally available for download at https://github.com/andrzejwichert/qai.
Allowing readers to learn the basic concepts of quantum computing on their home computers, this book is accessible to both the general readership as well as students and instructors of courses relating to computer science and AI.
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Table of contents
- Cover Page
- Half-Title Page
- Title Page
- Copyright Page
- Dedication Page
- Contents
- Preface
- Author
- Chapter 1 Artificial Intelligence
- Chapter 2 Quantum Physics and Quantum Computation
- Chapter 3 Qiskit
- Chapter 4 Quantum Gates
- Chapter 5 Grover's Amplification
- Chapter 6 SAT Problem
- Chapter 7 Symbolic State Representation
- Chapter 8 Quantum Production System
- Chapter 9 3 Puzzle
- Chapter 10 8 Puzzle
- Chapter 11 Blocks World
- Chapter 12 Five Pennies Nim Game
- Chapter 13 Basis Encoding of Binary Vectors
- Chapter 14 Quantum Associative Memory
- Chapter 15 Quantum Lernmatrix
- Chapter 16 Amplitude Encoding
- Chapter 17 Quantum Kernels
- Chapter 18 qRAM
- Chapter 19 Quantum Fourier Transform
- Chapter 20 Phase Estimation
- Chapter 21 Quantum Perceptron
- Chapter 22 HHL
- Chapter 23 Hybrid Approaches - Variational Classification
- Chapter 24 Conclusion
- Bibliography
- Index