Photonic Quantum Computing & Distributed Quantum Neural Networks

I study photonic quantum processors as a route toward scalable, room-ambient quantum information processing. My work links programmable linear optics, photon counting, tensor-network mappings, and distributed quantum neural-network training.

Current themes include:

• photonic QNNs for parameter-efficient learning and quantum-enhanced knowledge distillation;
• multi-QPU photonic learning workflows with deployable classical inference;
• distributed photonic quantum computing architectures for learning over quantum networks;
• hardware-aware evaluation of qumode capacity, sampling latency, optical-loop structure, and quantum–classical orchestration overhead.

Selected outputs: Nature Photonics 2026, IEEE QCE 2025, Imperial QuEST seed-funded projects.