Muhammad Haris

PROFESSIONAL PROFILE Please update objective section. EDUCATION BEE SEECS , Islamabad , 2.5 (2022) INTERNSHIP EXPERIENCE Nokia Alcatel Lucent 07-Jul-2025 - 12-Aug-2025 Understanding the cloud RAN deployement. Understanding the organizational structure and hirerarchy. Analysing sevice delivery mechanisms. FINAL YEAR PROJECT Analyzing Federated Learning Techniques in Quantum Computing Environments for Quantum Federated Learning (QFL) Applications Federated learning (FL) is an emerging paradigm that enables collaborative model training across multiple participants without the need to exchange raw data, thereby preserving privacy and security. With the rapid advancement of quantum computing, there is a growing interest in exploring how FL can be adapted to or executed on quantum platforms. This project proposes an in-depth analysis of federated learning on quantum computers, with the aim of identifying the opportunities, challenges, and feasibility of integrating these two technologies. Using Qiskit as a simulation framework, we will model distributed learning scenarios on quantum circuits, investigate the effects of noise and limited qubit resources, and analyze the scalability of quantum based FL approaches. The study will also benchmark quantum implementations against classical federated learning in terms of convergence, resource efficiency, and resilience to errors. The expected outcome of this work is a comprehensive understanding of the design considerations andpotential advantages of deploying federated learning within quantum computing environments, contributing toward the development of secure and scalable next-generation machine learning systems. TECHNICAL EXPERTISE Machine Learning and Networking Understanding of python,C++ and C.

Please update objective section.