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Quantum Artificial Intelligence with Qiskit

 
Edition number: 1
Publisher: Chapman and Hall
Date of Publication:
 
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GBP 74.99
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36 220 HUF (34 495 HUF + 5% VAT)
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Short description:

This book 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.

Long description:

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.

Table of Contents:

Preface
Author Bio
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