ABSTRACT

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.

chapter Chapter 1|22 pages

Artificial Intelligence

chapter Chapter 2|18 pages

Quantum Physics and Quantum Computation

chapter Chapter 3|22 pages

Qiskit

chapter Chapter 4|13 pages

Quantum Gates

chapter Chapter 5|15 pages

Grover's Amplification

chapter Chapter 6|11 pages

SAT Problem

chapter Chapter 7|4 pages

Symbolic State Representation

chapter Chapter 8|13 pages

Quantum Production System

chapter Chapter 9|20 pages

3 Puzzle

chapter Chapter 10|6 pages

8 Puzzle

chapter Chapter 11|12 pages

Blocks World

chapter Chapter 12|10 pages

Five Pennies Nim Game

chapter Chapter 13|16 pages

Basis Encoding of Binary Vectors

chapter Chapter 14|12 pages

Quantum Associative Memory

chapter Chapter 15|27 pages

Quantum Lernmatrix

chapter Chapter 16|14 pages

Amplitude Encoding

chapter Chapter 17|6 pages

Quantum Kernels

chapter Chapter 18|4 pages

qRAM

chapter Chapter 19|9 pages

Quantum Fourier Transform

chapter Chapter 20|9 pages

Phase Estimation

chapter Chapter 21|4 pages

Quantum Perceptron

chapter Chapter 22|11 pages

HHL

chapter Chapter 23|7 pages

Hybrid Approaches-Variational Classification

chapter Chapter 24|3 pages

Conclusion