Junaid Ashraf
Quantum Researcher & AI Research Analyst
Junaid Ashraf is a Quantum Researcher and AI Research Analyst specializing in Quantum Reservoir Computing, machine learning, and mathematical modeling. With a strong foundation in mathematics and scientific computing, he supports quantum-AI research initiatives and contributes to the Center of Excellence at Chinar Quantum AI.
His work spans quantum algorithm prototyping, data-driven modeling, visualization tools, and AI solution development. He actively bridges classical ML with emerging quantum paradigms through research, academic support, and technical content creation.
Experience
AI Research Analyst - Center of ExcellencePresent
Chinar Quantum AI
- Contributed to the development and structuring of the Center of Excellence (COE) framework.
- Worked on advanced research integrating quantum algorithms with classical AI methods.
- Collaborated with cross-functional teams to design quantum-enhanced AI prototypes.
- Provided strategic insights to advance quantum-classical hybrid applications.
Researcher & Support Team MemberPresent
Quantum Research & Academic Support
- Conducted research on Quantum Reservoir Computing (QRC) for time-series prediction tasks.
- Analyzed system dynamics and model accuracy for quantum-inspired frameworks.
- Supported QML training sessions by assisting participants with technical implementations.
- Developed presentations and content simplifying complex quantum concepts.
Education
B.Sc. (Hons) in Mathematics
Central University of Kashmir
Projects
Sensor Fault Detection
Built classification models on a semiconductor wafer dataset, performing preprocessing, feature engineering, and model optimization using Python and Scikit-learn.
Matrix Transformation Visualization App
Developed an interactive tool to visualize 2D/3D transformations including PCA and SVD using Plotly and NumPy.
Movie Recommendation System
Implemented a content-based recommendation engine using collaborative filtering, data analysis, and ML preprocessing techniques.