Principles of Quantum Artificial Intelligence

Book Description

In this book, we introduce quantum computation and its application to AI. We highlight problem solving and knowledge representation framework. Based on information , we cover two main of quantum computation — Quantum Fourier transform and Grover . 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 , theoretical computer science, quantum physics and computational physics.

Table of Contents

Chapter 1. Introduction
Chapter 2. Two Basic 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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