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Quantum Machine Learning for Finance: Advancing Financial Intelligence with Quantum Models

Kumar Prafull -
Table of Contents Introduction Why Use Quantum ML in Finance? Classical Financial ML Challenges QML Advantages in Financial Applications Encoding Financial Data into Quantum States Feature Mapping for Time Series...

Quantum Machine Learning for Chemistry: A New Paradigm in Molecular Modeling

Kumar Prafull -
Table of Contents Introduction Motivation for QML in Chemistry Classical Challenges in Quantum Chemistry What Makes Quantum ML Suitable for Chemistry? Representing Molecular Systems as Quantum Inputs Quantum Feature Maps...

Quantum Datasets and Benchmarks: Foundations for Evaluating Quantum Machine Learning

Kumar Prafull -
Table of Contents Introduction Why Datasets Matter in QML Classical vs Quantum Datasets Synthetic Datasets for Quantum ML Real-World Use Cases for Quantum Datasets Benchmarking in Classical ML vs QML Types...

Barren Plateaus and Training Issues in Quantum Machine Learning

Kumar Prafull -
Table of Contents Introduction What Are Barren Plateaus? Origins of Barren Plateaus in QML Mathematical Definition and Implications Why Barren Plateaus Hinder Training Expressibility vs Trainability Trade-off Quantum Circuit Depth and...

Quantum Feature Selection: Identifying Relevant Inputs for Quantum Machine Learning

Kumar Prafull -
Table of Contents Introduction Importance of Feature Selection in Machine Learning Challenges in Quantum Feature Selection Quantum Feature Maps and Encoding High-Dimensional Classical Features in QML Role of Feature Selection...

Quantum Overfitting and Regularization: Enhancing Generalization in Quantum Models

Kumar Prafull -
Table of Contents Introduction What Is Overfitting in Machine Learning? Manifestation of Overfitting in Quantum Models Sources of Overfitting in Quantum Machine Learning Variational Quantum Circuits and Model Complexity Role...

Gradient Descent in Quantum Landscapes: Navigating Optimization in Quantum Machine Learning

Kumar Prafull -
Table of Contents Introduction Understanding Quantum Loss Landscapes What Is Gradient Descent? Role of Gradients in Quantum Circuit Training Challenges Unique to Quantum Landscapes Variational Quantum Circuits and Cost Minimization The...

Auto-Differentiation in Quantum Circuits: Enabling Gradient-Based Quantum Machine Learning

Kumar Prafull -
Table of Contents Introduction What Is Auto-Differentiation? Why Gradients Matter in Quantum ML Variational Quantum Circuits and Parameter Training Challenges of Differentiation in Quantum Systems Classical vs Quantum Auto-Differentiation Forward and...

Optimization Techniques in Quantum Machine Learning: SPSA, COBYLA, and Beyond

Kumar Prafull -
Table of Contents Introduction Role of Optimization in Quantum Machine Learning Gradient-Based vs Gradient-Free Methods Stochastic Gradient Descent (SGD) Adam Optimizer Simultaneous Perturbation Stochastic Approximation (SPSA) SPSA: Algorithm and Use Cases SPSA...

Backpropagation with Parameter-Shift Rule in Quantum Models

Kumar Prafull -
Table of Contents Introduction Need for Gradients in Quantum ML Variational Quantum Circuits and Training Limitations of Classical Backpropagation The Parameter-Shift Rule: Core Concept Mathematical Derivation Conditions for Using Parameter-Shift Rule General...

Training Quantum Models: Optimizing Parameters for Quantum Machine Learning

Kumar Prafull -
Table of Contents Introduction What Does Training Mean in Quantum ML? Variational Quantum Circuits (VQCs) as Models Cost Functions and Objective Definitions Forward Pass: Circuit Evaluation Measurement and Output Processing Gradient...

Cost Functions for Quantum Models: Measuring Performance in Quantum Machine Learning

Kumar Prafull -
Table of Contents Introduction Role of Cost Functions in QML Characteristics of a Good Cost Function Cost Functions for Classification Binary Cross-Entropy Loss Mean Squared Error (MSE) Hinge Loss for Margin-Based...
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