Intuitive Learning of Quantum Machine Learning

This course offers an intuitive introduction to Quantum Machine Learning (QML) algorithms, combined with hands-on experience using IBM Qiskit and Pennylane platforms. Designed for those with an interest in AI and ML, the course does not require advanced knowledge of quantum mechanics, making it accessible to students from various backgrounds. It focuses on how quantum computing can enhance classical AI and ML computations, providing practical tools to leverage the power of quantum technologies.
 

Course Learning Outcomes:

• Gain knowledge in foundation of Classical AI, ML, and QML,
• Understand the basic QML enablers for AI ML,
• Gain intuitions to envision how QML can enhance classical AI ML problem solving,
• Develop hands-on skills with QML algorithms using IBM Qiskit software and Pennylane for QML

Course Highlights:

• Overview of AI Machine Learning and Quantum Computing Basics
• Overview of Quantum Machine Learning Algorithms Basics
• Universality Matrix Exponential & Rotation Gates
• Quantum Measurement and Density Matrix
• Tensor Products, Trace and Partial Trace
• Quantum Decomposition & Diagonalization
• Quantum Fourier Tansform (QFT) based Quantum Arithmetic
• Quantum Linear Algorithms, Principal Component Analysis (PCA), Singular Value Decomposition (SVD)
• Quantum Linear Equations Inversion, HHL algorithm
• Quantum Inner Products & Quantum Encoding
• Quantum Encoding and Quantum Counting
• Variational Quantum Algorithms & Quantum Neural Network (QNN)
• Quantum Support Vector Machine (QSVM) Algorithms
• Variational Quantum Algorithms Applications, VQE, VQC, VQLS, VQSD, VQPCA
• Quantum classifiers QML Data loading & Entanglement
• Hamiltonian Simulation Local, Sparse &Trotter-Suzuki
• Hamiltonian Simulation LCU, Random Walk