Module I
Introduction to Quantum Computing: Classical vs. Quantum Computing, Qubits, Superposition, Entanglement, Quantum Gates, Quantum Measurement, Basic Quantum Algorithms (6 Hours)
Module II
Fundamentals of Machine Learning: Supervised & Unsupervised Learning, Neural Networks, Optimization, Bias-Variance Tradeoff, Model Generalization (6 Hours)
Module III
Quantum Machine Learning: Quantum Data Encoding, Variational Quantum Circuits (VQCs), Quantum Kernel Methods, Quantum Neural Networks, Quantum GANs (8 Hours)
Module IV
Quantum Image and Signal Processing: Quantum Image Representation, Quantum Fourier Transform, Quantum Edge Detection, Quantum Image Compression (8 Hours)
Module V
Applications and Future Trends in Quantum Computing: Quantum Reinforcement Learning, Quantum Optimization, AI in Finance & Cryptography, Challenges & Future Research Directions. (8 Hours)

Understand the fundamental principles of quantum computing and its advantages over classical computing.

Explore the theoretical and practical aspects of quantum machine learning algorithms.

Develop the ability to implement quantum neural networks and variational quantum circuits.

Apply quantum techniques to real-world problems in image and signal processing.

(I) Demonstrate a solid understanding of quantum mechanics and quantum computing principles.
(II) Implement quantum machine learning algorithms using quantum programming frameworks such as Qiskit and Pennylane.
(III) Apply quantum feature extraction and classification techniques to complex datasets.
(IV) Utilize quantum computing techniques for image and signal processing tasks.

Nielsen, M. A., & Chuang, I. L., Quantum Computation and Quantum Information, Cambridge University Press

Schuld, M., & Petruccione, F., Supervised Learning with Quantum Computers, Springer

Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe & Seth Lloyd, Quantum Machine Learning, Nature

Carlo Ciliberto, Mark Herbster, Alessandro Davide Ialongo, Massimiliano Pontil, Andrea Rocchetto, Simone Severini and Leonard Wossnig, Quantum Machine Learning: A Classical Perspective, Royal Society