Principles of Quantum Artificial Intelligence

Book Description

In this book, we introduce quantum computation and its application to . We highlight problem solving and knowledge representation framework. Based on , 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 that is based on production .

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 : 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

Book Details

File HostFree Download LinkFormatSize (MB)Upload Date
EU(multi) Click to downloadPDF408/17/2014
UpLoaded Click to downloadPDF409/16/2014
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