Junaid Akhter
COO Global, Chinar Quantum AI
Junaid Akhter is a computational physicist and Quantum AI researcher specializing in artificial intelligence, deep learning, and quantum machine learning. He co-founded Chinar Quantum AI, where he drives innovation at the intersection of quantum computing and classical AI systems.
With experience across Germany's leading research institutes, he advances tensor networks, optimization theory, and physics-informed AI while building global collaborations and mentoring future AI professionals.
Experience
Chief Operating Officer - GlobalPresent
Chinar Quantum AI
- Leads development of next-generation Quantum AI solutions.
- Oversees multidisciplinary R&D teams across Quantum ML and computational technologies.
- Builds global collaborations with universities and industry partners.
- Develops scalable AI quantum hybrid applications.
- Promotes long-term AI ecosystem development for future generations.
AI ResearcherPresent
Technical University Dortmund
- Researches Quantum Machine Learning and Tensor Networks.
- Optimizes classical AI algorithms using quantum-inspired methods.
AI Researcher
Universität Paderborn
- Developed Physics-Informed AI models for industrial applications.
- Utilized top-tier European supercomputers for large-scale computations.
Student Researcher
Forschungszentrum Jülich
- Built Tensor-Network-based neural architectures for quantum systems.
- Improved computational efficiency using HPC platforms.
Education
Master of Physics
Universität Bonn
AI Researcher
Technical University Dortmund
AI Researcher
Universität Paderborn
Student Researcher
Forschungszentrum Jülich
Projects
Physics-Informed Neural Networks for Industrial Applications
Developed PINN-based frameworks for solving large-scale physics and engineering problems using HPC.
Tensor-Network-Based Neural Architectures
Created efficient neural network architectures using tensor networks for quantum simulations and high-dimensional systems.
Multi-Objective Optimization in Deep Learning
Advanced optimization techniques for industrial and scientific AI workloads using deep learning and HPC.