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Quantum ML Use Cases in Industry: Real-World Applications of Quantum-Enhanced Learning

Table of Contents Introduction Why Industry Is Exploring QML QML in Finance QML in Pharmaceuticals and Healthcare QML in Manufacturing and Logistics QML in Cybersecurity QML in Energy and Materials Science QML...
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Hosting Quantum ML Models: Deployment Strategies and Infrastructure

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Developing an End-to-End Quantum Machine Learning Application

Table of Contents Introduction Vision and Use Case Definition Data Pipeline Setup Feature Engineering for Quantum Encoding Quantum Circuit Design Hybrid Model Architecture Training Strategy and Optimization Evaluation Metrics and Baseline Comparison Hardware...
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Quantum Machine Learning Capstone Project Proposal: Design, Implementation, and Evaluation

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Hands-On Quantum Machine Learning with PennyLane

Table of Contents Introduction Why PennyLane for QML? Installation and Setup PennyLane Architecture and Philosophy Devices and Backends Constructing Quantum Circuits Encoding Classical Data into Quantum States Variational Quantum Circuits (VQCs) Building a...

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Experimenting with Quantum Machine Learning in Qiskit

Table of Contents Introduction Why Use Qiskit for QML? Qiskit Machine Learning Overview Installing and Setting Up Qiskit ML Qiskit Data Encoding Techniques Feature Map Circuits for Classification Variational Quantum Classifiers...

Software Frameworks for Quantum Machine Learning: Exploring PennyLane, TensorFlow Quantum, and More

Table of Contents Introduction Why Software Frameworks Matter in QML Overview of QML Framework Categories PennyLane: A Hybrid Quantum-Classical Framework Core Features of PennyLane Supported Interfaces and Backends Example Workflow in...

Security in Quantum ML Pipelines: Safeguarding Quantum-Enhanced Intelligence

Table of Contents Introduction Importance of Security in ML Pipelines Quantum ML: New Threat Landscape Attack Surfaces in Quantum ML Systems Adversarial Attacks on Quantum Models Parameter Manipulation in Variational...

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Training Quantum Models: Optimizing Parameters for Quantum Machine Learning

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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

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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...

Variational Circuits in ML Workflows: Quantum Layers for Learnable Representations

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Table of Contents Introduction What Are Variational Quantum Circuits (VQCs)? Why Use VQCs in Machine Learning? Structure of a Variational Circuit Parameterized Quantum Gates Designing Expressive Circuit Architectures Encoding Classical Data...

Quantum Reinforcement Learning: Merging Quantum Computing with Adaptive Decision Making

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Table of Contents Introduction Classical Reinforcement Learning Overview What is Quantum Reinforcement Learning (QRL)? Why Quantum for Reinforcement Learning? QRL Frameworks and Paradigms Quantum Agents and Environments Quantum Policy Representation Quantum Value...

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

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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

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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

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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

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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

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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

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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

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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

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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...
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