Advanced Quantum Machine Learning Algorithms and Implementation
Chapter 1: Revisiting Quantum Computing and ML Foundations
Advanced Linear Algebra for Quantum States
Quantum Circuits and Universal Gate Sets Revisited
Entanglement and Non-locality Implications for QML
Density Matrices and Mixed States in QML
Classical ML Optimization Techniques Refresher
Information Geometry in Classical and Quantum Models
Complexity Theory for Classical and Quantum Algorithms
Chapter 2: Advanced Quantum Data Encoding and Feature Maps
Mathematical Foundations of Quantum Feature Maps
Higher-Order Polynomial Quantum Feature Maps
Data Re-uploading Techniques for Expressivity
Analyzing Feature Map Expressibility and Entangling Capability
Kernel Alignment and Quantum Feature Spaces
Encoding High-Dimensional Classical Data
Hands-on Practical: Implementing Custom Quantum Feature Maps
Chapter 3: Quantum Kernel Methods: Theory and Implementation
The Quantum Kernel Trick Formalism
Calculating Quantum Kernel Matrices
Properties of Quantum Kernels
Geometric Differences between Classical and Quantum Kernels
Kernel Concentration Phenomena and Mitigation
Support Vector Machines with Quantum Kernels (QSVM)
Implementation of QSVM for Complex Datasets
Hands-on Practical: Comparing Quantum Kernels
Chapter 4: Variational Quantum Algorithms for Machine Learning
The Variational Principle in Quantum Computation
Parameterized Quantum Circuits (PQC) Design Strategies
Cost Functions for QML Tasks
Gradient Calculation Methods
Advanced Classical Optimizers for VQAs
Quantum Natural Gradient Descent
Barren Plateaus Analysis and Mitigation
Hands-on Practical: Implementing a Variational Quantum Classifier
Chapter 5: Quantum Neural Networks: Architectures and Training
Models of Quantum Neurons and Layers
Quantum Circuit Born Machines (QCBMs)
Quantum Convolutional Neural Networks (QCNNs)
Quantum Graph Neural Networks (QGNNs)
Hybrid Quantum-Classical Neural Network Architectures
Training QNNs Challenges and Strategies
Overfitting and Generalization in QNNs
Practice: Building and Training a Simple QNN
Chapter 6: Quantum Generative Models
Classical Generative Models Review
Quantum Circuit Born Machines for Distribution Learning
Quantum Generative Adversarial Networks (QGANs)
Training QGANs Challenges and Architectures
Evaluating Quantum Generative Models
Sampling from Quantum Generative Models
Hands-on Practical: Implementing a Basic QGAN
Chapter 7: Hardware Considerations and Error Mitigation in QML
Characterizing Quantum Hardware Noise
Impact of Noise on QML Algorithm Performance
Quantum Error Mitigation Techniques
Circuit Optimization and Transpilation
Hardware-Efficient Ansätze Design
Benchmarking QML Algorithms on Real Quantum Devices
Practice: Applying Error Mitigation to a VQC
Quantum Generative Adversarial Networks (QGANs)
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- Quantum Generative Adversarial Networks for learning and loading quantum data, Christa Zoufal, Aurélien Lucchi, and Stefan Woerner, 2019 npj Quantum Information, Vol. 5 (Springer Nature) DOI: 10.1038/s41534-019-0220-9 - Introduces and experimentally demonstrates the concept of Quantum Generative Adversarial Networks (QGANs), including the QC-GAN architecture, for learning and loading quantum data distributions.
- Parameterized quantum circuits as machine learning models, Marcello Benedetti, Erika Lloyd, Stefan Sack, and Mattia Fiorentini, 2019 Quantum Science and Technology, Vol. 4 (IOP Publishing) DOI: 10.1088/2058-9565/ab4eb5 - Provides a comprehensive overview of parameterized quantum circuits (PQCs) and their application as machine learning models, highly relevant for understanding the quantum generator.
- Evaluating gradients of parametrized quantum circuits with the parameter-shift rule, Maria Schuld, Alexey I. Khazov, David Pérez-García, and Vedran Dunjko, 2020 Physical Review A, Vol. 101 (American Physical Society) DOI: 10.1103/PhysRevA.101.032311 - A foundational paper detailing the parameter-shift rule, an essential technique for calculating gradients in variational quantum algorithms, including QGANs.
- Barren plateaus in quantum neural network training landscapes, Jarrod R. McClean, Sergio Boixo, Vadim N. Smelyanskiy, Ryan Babbush, and Hartmut Neven, 2018 Nature Communications, Vol. 9 DOI: 10.1038/s41467-018-07090-4 - Identifies and explains the phenomenon of barren plateaus, a significant challenge in training deep parameterized quantum circuits, relevant for generator design.