Hybrid Machine Leanring for Big Data

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DS09
Semester: 3rd,
ECTS Credits: 7.5

(Course Coordinator)

Syllabus

Introduction to Quantum Mechanics
Probabilities and Complex Functions
Introduction to Quantum Computing
Qubits, Registers, and Circuits
State Vector, Quantum Entanglement, Simple Quantum Algorithms
No-Cloning Theorem and Quantum Teleportation
The Deutsch–Jozsa and Bernstein–Vazirani Algorithms
Modern Trends in Classical Machine Learning (data embedding techniques, feature selection/extraction techniques, dimensionality reduction)
Introduction to Quantum Machine Learning
Encoding Classical Data into Quantum States
Variational Quantum Algorithms (VQA)
Principles of Hybrid Quantum-Classical Algorithms, Efficiency (which parts of the algorithm are implemented classically and which quantumly)
Implementation of Hybrid Quantum-Classical Algorithms and Their Evaluation

Ηλίας Κ. Σάββας και Μαρία Σαμπάνη, «Κβαντική Υπολογιστική: από την θεωρία στην πράξη», Εκδόσεις Τζιόλα, 2022
Michael A. Nielsen, and Isaac L. Chuang, “Quantum Computation and Quantum Information”, Cambridge University Press, 2010
Eleanor Rieffel and Wolfgang Polak, “Quantum Computing: A Gentle Introduction”, The MIT Press, 2011
Macauley Coggins, “Introduction to Quantum
Computing with Qiskit”, Scarborough Quantum Computing Ltd, 2021
Schuld, M., & Petruccione, F. Machine learning with quantum computers. Berlin: Springer. 2021
Ganguly, S. Quantum Machine Learning: An Applied Approach. Apress. 2021
Qiskit, I., & Pattanayak, S. Quantum Machine Learning with Python. 2021