Quantum + AI

I work at the frontier of Quantum Artificial Intelligence, building learning systems that combine the representational power of classical AI with the physics of quantum computation. My research focuses on Quantum Reinforcement Learning, Quantum Neural Networks, and self-programming quantum architectures aimed at scalable, adaptive, and generalizable intelligence.

I currently serve as a Lead Research Scientist in industry while maintaining active collaborations with national laboratories and universities worldwide. Previously, I was a Computational Scientist at Brookhaven National Laboratory, where I helped pioneer frameworks for Quantum Machine Learning and cross-domain applications spanning physics, biomedicine, and intelligent systems.

My work has led to more than 100 publications across IEEE, APS, IOP, and major AI venues, along with invited tutorials and workshops at flagship conferences. I have organized international programs on Distributed Quantum Machine Learning, Quantum Reinforcement Learning, and Quantum AI for Communication Networks, and I serve on multiple editorial boards including IEEE Transactions and IEEE CAS Magazine.

Research interests:

• Quantum Reinforcement Learning

• Quantum LSTM and Quantum Time-Series Intelligence

• Quantum Architecture Search

• Federated & Distributed Quantum Machine Learning

• Quantum-Classical Hybrid Agents and Meta-Learning

• Applications to critical infrastructure, finance, and scientific discovery

I believe the next breakthrough in AI will not come from scaling alone, but from new computational principles. My goal is to build systems that learn, reason, and self-improve at the intersection of algorithms and quantum physics—and to help shape a global ecosystem where Quantum AI becomes practical, trustworthy, and transformative.

Open to academic collaborations, invited talks, and joint projects that push the boundaries of intelligent machines.

 Research Fields

Selected work