Muhammad Haris

Academic

Career

Verbatim text

Muhammad Haris
Cell: 923314666317 |  Email: m.haris.2332@gmail.com
Address: HOUSE NO.444 , STREET NO.90, SECTOR ,PAKISTANG-9/4,ISLAMABAD , Islamabad , Pakistan
PROFESSIONAL PROFILE
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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, 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.

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