Course

Unit 1

Introduction to Quantum Computing: Classical vs. Quantum Computing, Qubits, Superposition, Entanglement, Quantum Gates, Quantum Measurement, Basic Quantum Algorithms.

Unit 2

Fundamentals of Machine Learning: Supervised & Unsupervised Learning, Neural Networks, Optimization, Bias-Variance Tradeoff, Model Generalization

Unit 3

Quantum Machine Learning: Quantum Data Encoding, Variational Quantum Circuits (VQCs), Quantum Kernel Methods, Quantum Neural Networks, Quantum GANs.

Unit 4

Quantum Image and Signal Processing: Quantum Image Representation, Quantum Fourier Transform, Quantum Edge Detection, Quantum Image Compression (8 Hours)

Unit 5

Applications and Future Trends in Quantum Computing: Quantum Reinforcement Learning, Quantum Optimization, AI in Finance & Cryptography, Challenges & Future Research Directions.

Course Objective

• 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.

Course Outcomes:

• CO1:  Demonstrate a solid understanding of quantum mechanics and quantum computing principles.
• CO2: Implement quantum machine learning algorithms using quantum programming frameworks such as Qiskit and Pennylane.
• CO3: Apply quantum feature extraction and classification techniques to complex datasets.
• CO4: Utilize quantum computing techniques for image and signal processing tasks.

Text books:

1. Nielsen, M. A., & Chuang, I. L., Quantum Computation and Quantum Information, Cambridge University Press
2. Schuld, M., & Petruccione, F., Supervised Learning with Quantum Computers, Springer

Reference books:

1. Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe & Seth Lloyd, Quantum Machine Learning, Nature
2. Carlo Ciliberto, Mark Herbster, Alessandro Davide Ialongo, Massimiliano Pontil, Andrea Rocchetto, Simone Severini and Leonard Wossnig, Quantum Machine Learning: A Classical Perspective, Royal Society

CO-PO Mapping: