Research

Research – Quantum Optics for the Future

We work at the intersection of quantum optics, AI, and photonics to build practical quantum computing systems that run at room temperature. 

Our goal: Make quantum error correction (QEC) fast, scalable, and energy-efficient.

Core Research Areas

1. AI-Driven Quantum Error Correction

• Quantum computers fail without error correction. Current codes are too slow for real-time operation.  

• We develop machine learning decoders for surface/toric codes that run 10x faster than classical methods. 

Student Projects:

- Python-based QEC simulation

- Real-time decoder benchmarking (Stim + PyMatching)

- Threshold calculation for fault-tolerant quantum memory

2. Optical Neuromorphic Computing

• AI training consumes massive energy. Traditional neural networks are power-hungry.  

• We develop the diffractive deep neural networks (D2NN) using microring resonators for all-optical computing. 

Student Projects:

- Microring resonator design (Lumerical FDTD simulation)

- Optical backpropagation algorithm development

- Hardware implementation with silicon photonics

3. Metamaterial-Based Quantum Gates

• Quantum gates require cryogenic temperatures. Room-temperature operation is our goal.  

• We develop topological photonic crystals for CNOT gates using nonlinear optical materials. 

Student Projects:

- SEM/TEM image analysis of metamaterial samples

- Nonlinear optical characterization (Z-scan method)

- Gate fidelity simulation (COMSOL Multiphysics)

Impact & Applications

• AI: Energy-efficient optical neural networks for edge devices

• Quantum Tech: Scalable error correction for fault-tolerant computing

• Healthcare: Quantum-enhanced cancer diagnostics

Last updated: December 2025