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Hybrid Neural Networks: Merging Classical and Quantum Models for Intelligent Learning

Kumar Prafull -
Table of Contents Introduction What Are Hybrid Neural Networks? Why Combine Classical and Quantum Layers? General Architecture of Hybrid Models Quantum Layers in Classical Pipelines Classical Preprocessing and Postprocessing Variational Quantum...

Data Re-uploading Strategies in Quantum Machine Learning

Kumar Prafull -
Table of Contents Introduction The Challenge of Expressivity in Quantum Circuits What Is Data Re-uploading? Motivation Behind Data Re-uploading Mathematical Foundation of Re-uploading Circuit Architecture with Re-uploading Implementation Techniques Periodic vs Adaptive...

Quantum GANs – Generative Adversarial Networks: Quantum Approaches to Data Generation

Kumar Prafull -
Table of Contents Introduction Classical GANs: A Brief Overview Motivation for Quantum GANs Structure of a Quantum GAN (QGAN) Quantum Generator: Circuit-Based Design Quantum Discriminator Options Hybrid Classical-Quantum Architectures Objective Functions and...

Quantum Boltzmann Machines: Quantum Models for Probabilistic Learning

Kumar Prafull -
Table of Contents Introduction Classical Boltzmann Machines Recap From Classical to Quantum Boltzmann Machines Structure of a Quantum Boltzmann Machine (QBM) Quantum Energy-Based Models Hamiltonian Representation in QBM Quantum States as...

Quantum Principal Component Analysis (qPCA): Dimensionality Reduction with Quantum States

Kumar Prafull -
Table of Contents Introduction What Is Principal Component Analysis (PCA)? Motivation for Quantum PCA Quantum Representation of Covariance Matrices The qPCA Algorithm: Core Ideas Quantum Density Matrix as Covariance Proxy Step-by-Step...

Quantum Support Vector Machines: Leveraging Quantum Kernels for Pattern Classification

Kumar Prafull -
Table of Contents Introduction Classical Support Vector Machines (SVMs) Motivation for Quantum SVMs Quantum Kernels in SVMs Quantum Feature Mapping Quantum Kernel Matrix Estimation SVM Decision Function with Quantum Kernels Training Quantum...

Quantum Nearest-Neighbor Models: Leveraging Quantum Metrics for Pattern Recognition

Kumar Prafull -
Table of Contents Introduction Classical k-Nearest Neighbors (k-NN) Overview Motivation for Quantum k-NN (QkNN) Quantum State Similarity Measures Encoding Classical Data into Quantum States Distance Metrics in Quantum Space Quantum Fidelity...

Variational Quantum Classifiers: A Hybrid Approach to Quantum Machine Learning

Kumar Prafull -
Table of Contents Introduction What Are Variational Quantum Classifiers (VQCs)? Why Use Variational Circuits for Classification? Key Components of a VQC Quantum Data Encoding Ansatz Design for Classification Measurement and Output...

Feature Maps and Quantum Kernels: Enhancing Machine Learning with Quantum Embeddings

Kumar Prafull -
Table of Contents Introduction Classical Feature Maps and Kernels Why Quantum Feature Maps? Basics of Quantum Kernel Methods Embedding Data into Hilbert Space Types of Quantum Feature Maps ZZFeatureMap PauliFeatureMap Custom Feature Maps...

Encoding Classical Data into Quantum States: Foundations and Techniques

Kumar Prafull -
Table of Contents Introduction Why Encoding Matters in Quantum Machine Learning Characteristics of Quantum Data Representations The Challenge of Data Input in QML Types of Quantum Data Encoding Basis Encoding Amplitude...

Classical vs Quantum ML Approaches: A Comparative Overview

Kumar Prafull -
Table of Contents Introduction Defining Classical Machine Learning Defining Quantum Machine Learning Theoretical Foundations of Classical ML Theoretical Foundations of Quantum ML Data Representation in Classical vs Quantum ML Model Architectures...

Introduction to Quantum Machine Learning: Merging Quantum Computing with AI

Kumar Prafull -
Table of Contents Introduction What Is Quantum Machine Learning (QML)? Why Combine Quantum and Machine Learning? Key Concepts Behind QML Qubits, Superposition, and Entanglement Classical vs Quantum ML Pipelines Data Encoding...

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