Areas of Expertise.
Quantum/Machine Learning (QML)
Applying quantum algorithms (e.g., QCNN, VQC, Neural Network Classifier) to accelerate classical ML tasks and utilizing variational methods in Qiskit, TensorCircuit and PennyLane.
Quantum/Federated Learning (FL)
Developing secure, privacy-preserving distributed systems and analyzing QFL architectures.
Distributed Systems & Edge AI
Designing robust, scalable architectures for decentralized learning and optimizing models for edge deployment and data handling.
Quantum/Cryptography
Explore and study Post-Quantum Cryptography (PQC), quantum key distribution (QKD), and secure multi-party computation (MPC) protocols.
Quantum/Privacy (Differential Privacy)
Implementing privacy-enhancing technologies like Differential Privacy, Data Condensation, Pruning, SVD, QKD, Noise in decentralized systems.
Quantum Computing & Simulation
Familiarity with quantum hardware (IBM Qiskit, PennyLane, TensorCircuit), quantum noise, quantum error correction, and simulating quantum circuits.
Wireless Communication
AWGN, RayLeigh Fading, MATLAB Tools, Deep Reinforcement Learning, Channel State Information
Developer Resources.
Technical Proficiency.
Programming & Core Development
Python (Advanced) | C/C++ | Java | R | JavaScript (Node.js) | Bash/Shell
Quantum & Quantum ML
Qiskit | PennyLane | TensorCircuit | Google Cirq | Quantum Circuit Simulation | VQE, QCNN, QVAE
Machine Learning & Data Science
PyTorch | TensorFlow | Scikit-learn | Pandas | NumPy | Causal Inference | Time-Series Analysis
Distributed & Backend Systems
Django | Flask | REST Frameworks | PostgreSQL | MongoDB | MySQL | Docker | Blockchain/DLT
Web/Mobile Tools
Java | Kotlin | IOS | React | Angular | Next.js | Vue.js | HTML/CSS | Git | Visual Studio Code
Research, Documentation
LaTeX (Overleaf) | Git
Latest Publications
Chained Continuous Quantum Federated Learning Framework
2025Pioneering work on a continuous QFL framework that uses chained models for enhanced efficiency and resource optimization. (Q1, Core A Journal)
Quantum Federated Learning for Metaverse: Analysis, Design and Implementation
2025Comprehensive study and implementation of QFL architectures optimized for low-latency, high-security Metaverse requirements.
Performance Analysis and Design of a Weighted Personalized Quantum Federated Learning
2025Designing a novel weighted personalization strategy for QFL to improve model accuracy and fairness across heterogeneous client data distributions. (Q1 Journal)
Performance analysis and evaluation of post-quantum secure blockchained federated learning
2024Evaluation of integrating Post-Quantum Cryptography (PQC) with Blockchain-based Federated Learning to ensure long-term security. (Q1, Core A Journal)