AI-Assisted Quantum Algorithm Discovery
Computational approaches for searching, simplifying, and interpreting quantum circuits and for identifying reusable principles of quantum algorithm design.
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Clark Atlanta University
Mohammad Javani, Ph.D.
Assistant Professor of Cyber-Physical Systems
My research connects quantum computing, artificial intelligence, nanophotonics, and scientific computing to build efficient, interpretable computational methods for complex physical systems.
About
I am a tenure-track Assistant Professor in the Department of Cyber-Physical Systems at Clark Atlanta University. My work spans quantum algorithms, quantum simulation, artificial intelligence for scientific discovery, nanophotonic inverse design, explainable machine learning, and quantum cybersecurity.
A recurring question across my research is how much computational complexity is actually required to solve a scientific problem. I am interested not only in obtaining accurate predictions, but also in identifying the structures, representations, and physical principles that make those predictions possible.
Research Themes
Current Directions
Quantum Simulation
Variational and reduced quantum representations for atomic, molecular, photonic, and quantum-material Hamiltonians, with emphasis on reproducibility and resource efficiency.
Read more →AI for Nanophotonics
Machine learning for inverse design, surrogate modeling, and complexity reduction in multilayers, metasurfaces, gratings, and optical sensing structures.
Read more →Explainable Scientific Machine Learning
Feature importance, pruning, dimensionality reduction, and latent representations for understanding what scientific neural networks actually learn.
Read more →Quantum Cybersecurity
Quantum-generated entropy, cyber deception, moving-target defense, and hybrid quantum-classical approaches for resilient cyber-physical systems.
Read more →Research Philosophy
Accuracy, Efficiency, and Interpretability
Many computational methods can produce accurate answers. A more difficult scientific question is whether we can understand why they work and what complexity is truly necessary.
- AccuracyReliable scientific predictions and reproducible computational results.
- EfficiencyReduced classical and quantum resource requirements without sacrificing essential physics.
- InterpretabilityUnderstanding the physical and computational structures behind successful models.
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Student Research Opportunities
Undergraduate and graduate students interested in quantum computing, AI, computational physics, nanophotonics, or quantum cybersecurity are encouraged to explore current opportunities.