Title: Five Day FDP on “Quantum Machine Learning: Scope for Real-World Problems”

Date: March 10, 2026, to March 14, 2026

About the FDP:

Quantum Machine Learning (QML) represents a developing interdisciplinary research area that integrates the foundations of quantum computing with machine learning methodologies to address computationally complex real-world problems. With ongoing progress in quantum hardware, software platforms, and algorithmic design, QML is increasingly recognized for its potential applications in domains such as optimization, financial analysis, healthcare informatics, cybersecurity, and large-scale data processing.

This Faculty Development Programme (FDP) is structured to provide participants with a comprehensive conceptual understanding of quantum computing principles and their relevance to machine learning. The programme examines the scope, potential benefits, and inherent limitations of Quantum Machine Learning, while emphasizing practical problem contexts where quantum-enabled approaches may offer computational advantages. Expert lectures and analytical discussions will support participants in understanding contemporary developments, research challenges, and emerging directions in the field of QML.

Furthermore, the FDP seeks to encourage faculty members and researchers to integrate Quantum Machine Learning concepts into teaching methodologies, research activities, and curriculum development, thereby fostering academic preparedness for next-generation computational technologies.

FDP Objectives:

1.Understand the fundamentals of quantum computing and its relevance to machine learning.

2.Explore the core concepts of Quantum Machine Learning (QML)

3.Compare quantum machine learning vs classical machine learning with advantages and limitations.

4.Identify real-world problem domains in Quantum Machine Learning

5.Understand the current quantum algorithms used in Machine Learning