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About the book
This comprehensive book aims to provide readers with a deep understanding of the intersection between quantum computing and machine learning. From foundational concepts to advanced algorithms, readers will learn how to implement quantum machine learning techniques using Python and other quantum programming languages. The book also explores real-world applications across various industries and discusses the challenges and future directions of quantum machine learning. With detailed explanations, practical examples, and code implementation, readers will gain the knowledge and skills to explore the cutting-edge field of quantum computing in machine learning.
Chapter 1: Introduction to Quantum Computing 1.1 The Need for Quantum Computing 1.2 Fundamental Principles of Quantum Mechanics 1.3 Quantum Gates and Quantum Circuits 1.4 Quantum Algorithms and Complexity Theory 1.5 Quantum Hardware Overview 1.6 Quantum Simulators and Quantum Cloud Services
Chapter 2: Classical Machine Learning Fundamentals 2.1 Introduction to Machine Learning 2.2 Supervised Learning 2.3 Unsupervised Learning 2.4 Reinforcement Learning 2.5 Deep Learning and Neural Networks 2.6 Evaluation Metrics and Model Selection
Chapter 3: Quantum Machine Learning Basics 3.1 Quantum Bits (Qubits) and Quantum States 3.2 Quantum Gates and Quantum Circuits for Machine Learning 3.3 Quantum Data Encoding Techniques 3.4 Quantum Algorithms for Classification and Regression 3.5 Quantum Algorithms for Clustering and Dimensionality Reduction 3.6 Quantum Generative Models and Quantum Variational Algorithms
Chapter 4: Implementing Quantum Machine Learning with Python 4.1 Introduction to Quantum Programming Languages 4.2 Quantum Computing Libraries in Python (Qiskit, Cirq, and PyQuil) 4.3 Quantum Circuit Design and Simulation in Python 4.4 Quantum Data Encoding and Pre-processing 4.5 Implementing Quantum Machine Learning Algorithms in Python 4.6 Debugging and Optimizing Quantum Circuits
Chapter 5: Quantum Support Vector Machines (QSVM) 5.1 Classical Support Vector Machines (SVM) 5.2 Quantum Support Vector Machines: Theory and Implementation 5.3 Training and Evaluating QSVM Models 5.4 Enhancements and Variations of QSVM
Chapter 6: Quantum Neural Networks (QNN) 6.1 Introduction to Neural Networks and Deep Learning 6.2 Quantum Neural Networks: Architecture and Operations 6.3 Quantum Gradient Descent and Training QNNs 6.4 Quantum Variational Circuits for QNNs 6.5 Implementing Quantum Convolutional Neural Networks
Chapter 7: Quantum Data Clustering and Dimensionality Reduction 7.1 Classical Clustering Algorithms (K-means, DBSCAN, etc.) 7.2 Quantum Clustering Algorithms: Theory and Implementation 7.3 Quantum Dimensionality Reduction Techniques 7.4 Comparing Quantum and Classical Clustering Results
Chapter 8: Quantum Generative Models and Data Synthesis 8.1 Introduction to Generative Models (GANs, VAEs, etc.) 8.2 Quantum Generative Models: Theory and Algorithms 8.3 Quantum Data Synthesis and Sample Generation 8.4 Applications of Quantum Generative Models
Chapter 9: Real-World Applications of Quantum Machine Learning 9.1 Quantum Machine Learning in Drug Discovery 9.2 Quantum Finance and Portfolio Optimization 9.3 Quantum Machine Learning for Image and Text Analysis 9.4 Quantum Machine Learning in Smart Cities and Transportation 9.5 Quantum Machine Learning for Quantum Chemistry
Chapter 10: Challenges and Future Directions 10.1 Overcoming Quantum Hardware Limitations 10.2 Error Correction and Noise Mitigation 10.3 Developing Efficient Quantum Algorithms for Machine Learning 10.4 Quantum Machine Learning in the Era of Quantum Supremacy 10.5 Ethical Considerations and Implications of Quantum Machine Learning
ASIN : B0CB965R98
Publication date : July 5, 2023
Language : English
File size : 2151 KB
Simultaneous device usage : Unlimited
Text-to-Speech : Enabled
Screen Reader : Supported
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 350 pages
