Quantum Machine Learning Algorithms and Applications
Table Of Contents
1. Introduction
1.1 Background and Motivation
1.2 Objectives of the Project
2. Quantum Computing Fundamentals
2.1 Quantum Bits and Quantum Gates
2.2 Quantum Circuit Model
3. Machine Learning Basics
3.1 Supervised Learning
3.2 Unsupervised Learning
3.3 Reinforcement Learning
4. Quantum Machine Learning Algorithms
4.1 Quantum Support Vector Machines
4.2 Quantum Neural Networks
4.3 Quantum Clustering Algorithms
5. Applications of Quantum Machine Learning
5.1 Quantum Chemistry and Drug Discovery
5.2 Financial Portfolio Optimization
5.3 Quantum Machine Learning for Big Data
Thesis Abstract
Quantum computing has the potential to revolutionize machine learning by offering exponential speedup for certain algorithms. This project aims to explore the intersection of quantum computing and machine learning, focusing on the development and application of quantum machine learning algorithms. The project will investigate how quantum computing can be leveraged to enhance traditional machine learning tasks such as classification, clustering, and regression. Additionally, the project will explore potential applications of quantum machine learning in areas such as drug discovery, financial modeling, and optimization problems.
Thesis Overview
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