Research
Research
My work develops quantum algorithms, distributed quantum-computing systems, quantum machine learning workflows, quantum-HPC benchmarking methods, photonic quantum technologies, and practical quantum sensing techniques. The common objective is to move quantum technologies from isolated proof-of-principle demonstrations toward scalable, benchmarked, and deployable computational systems.
Six research directions
Distributed quantum computing
Network-aware compilation, circuit partitioning, entanglement scheduling, and resource orchestration for modular quantum processors.
Quantum machine learning
Quantum kernels, quantum neural networks, parameter-efficient learning, quantum knowledge distillation, and robust learning pipelines.
Quantum-HPC and benchmarking
Tensor-network simulation, GPU-accelerated workflows, application-driven benchmarks, and system-level performance metrics.
Photonic quantum computing
Photonic processors, distributed photonic learning, optical neural-network models, and nonlinear photonic resources.
Quantum algorithms for optimisation
Hybrid quantum-classical optimisation for finance, power systems, wireless networks, and industrial decision problems.
Practical quantum sensing technologies
Spin-based sensing, optically detected resonance, microwave measurement, acoustic interfaces, and room-temperature molecular platforms.