In this book, we introduce quantum computation and its application to AI. We highlight problem solving and knowledge representation framework. Based on information theory, we cover two main principles of quantum computation — Quantum Fourier transform and Grover search. Then, we indicate how these two principles can be applied to problem solving and finally present a general model of a quantum computer that is based on production systems.
Readership: Professionals, academics, researchers and graduate students in artificial intelligence, theoretical computer science, quantum physics and computational physics.
Table of Contents
Chapter 1. Introduction
Chapter 2. Two Basic Data Mining Methods for Variable Assessment
Chapter 3. CHAID-Based Data Mining for Paired-Variable Assessment
Chapter 4. The Importance of Straight Data: Simplicity and Desirability for Good Model-Building Practice
Chapter 5. Symmetrizing Ranked Data: A Statistical Data Mining Method for Improving the Predictive Power of Data
Chapter 6. Principal Component Analysis: A Statistical Data Mining Method for Many-Variable Assessment
Chapter 7. The Correlation Coefficient: Its Values Range between Plus/Minus 1, or Do They?
Chapter 8. Logistic Regression: The Workhorse of Response Modeling
Chapter 9. Ordinary Regression: The Workhorse of Profit Modeling