Quantum AI Scientist

Quantum algorithms for networked quantum systems.

My work asks how quantum algorithms can become useful on networked, photonic, and early fault-tolerant platforms. I develop distributed quantum computing architectures, quantum machine learning methods, quantum-HPC benchmarking workflows, and hardware-aware compilation techniques that connect algorithm design with realistic quantum systems.

Quantum AIquantum learning, kernels, and scientific AI workflows
DQCnetworked architectures, compilation, and orchestration
Q-HPCbenchmarking, simulation, and resource analysis
Photonic QCphotonic learning and nonlinear quantum resources
Latest updates

Latest news

Extensible universal photonic quantum computing with nonlinearity accepted in Nature Photonics.

Collaborative work on scalable photonic quantum computing and nonlinear resources.

Heterogeneous optically detected spin–acoustic resonance accepted in Communications Physics.

Molecular thin-film spin–acoustic interfaces for practical quantum sensing technologies.

Adaptive resource orchestration for distributed quantum computing systems accepted in IEEE Internet Computing.

System-level orchestration methods for quantum networks and modular quantum processors.

Distributed photonic quantum neural networks recognised at IEEE QCE.

Best/distinguished paper activity on distributed quantum learning and photonic quantum computing.

Research portfolio

Research themes

My research connects algorithmic design, quantum systems engineering, and deployable software workflows for networked and heterogeneous quantum processors.

01

Distributed quantum computing

Compilation, resource orchestration, network-aware circuit partitioning, and entanglement-efficient execution across modular processors.

02

Quantum machine learning

Quantum kernels, quantum neural networks, parameter-efficient quantum learning, and robust learning workflows for scientific and industrial data.

03

Quantum-HPC and benchmarking

Large-scale simulation, quantum software benchmarking, tensor-network workflows, and hardware-aware performance analysis.

04

Photonic quantum computing

Photonic processors, distributed photonic learning, optical quantum neural networks, and nonlinear resources for scalable architectures.

05

Quantum algorithms for optimisation

Hybrid quantum-classical optimisation, QUBO modelling, finance, energy systems, and combinatorial decision workflows.

06

Practical quantum sensing technologies

Spin-based sensing, optically detected resonance, microwave measurement, acoustic interfaces, and room-temperature molecular platforms.

Distributed QCnetwork-aware compilation and resource orchestration
Photonic QClearning workflows and nonlinear resources
Quantum-HPCsimulation, benchmarking, and resource estimation
QMLscientific AI and industrial data applications
Selected outputs

Selected publications

IEEE QCE 2025

Distributed Quantum Neural Networks on Distributed Photonic Quantum Computing

Distributed quantum learning on photonic quantum computing architectures.

IEEE Internet Computing · 2026

Adaptive Resource Orchestration for Distributed Quantum Computing Systems

Orchestration and system design for distributed quantum computing.

Communications Physics · 2026

Heterogeneous Optically Detected Spin–Acoustic Resonance in Molecular Thin Films

Spin–acoustic interfaces for practical quantum sensing technologies.

Contact

Collaboration and research enquiries

I am interested in collaborations on distributed quantum computing, quantum-HPC benchmarking, quantum machine learning, photonic quantum computing, and practical quantum sensing technologies.

Last updated: June 2026