Research Programmes

Core Focus

Research Programmes

Active research across quantum simulation, machine learning, climate science and high-performance computing.

Q-Nucleation and Q-Condensation

Quantum-accurate binding energies for cloud seeding optimisation across 297 molecular systems spanning 8 chemistry domains.

Timeline: Jul to Sep 2026

Summary

A systematic VQE campaign across 297 molecular systems spanning 8 chemistry domains, including transition metal oxides (175 from the Materials Project), CO2 catalysis surfaces (48 configurations), OER catalysts for green hydrogen, photocatalytic surfaces, cement CSH phases, nitrification enzyme variants, and battery cathode materials. Binding energy and condensation efficiency landscapes are computed to identify optimal cloud seeding materials.

Outputs

Batch VQE pipeline producing 406 result files across CASCI classical references and VQE batches; binding energy and condensation efficiency landscapes; open dataset published on Zenodo (CC-BY 4.0); methodology paper on arXiv (quant-ph).

Multi-Source Data Fusion with Quantum-Enhanced Classifiers

Quantum-enhanced classifiers fusing satellite imagery, sensor networks, and climate model outputs into unified detection systems.

Timeline: Jul to Sep 2026

Summary

The project develops quantum kernel methods and QNN-based fusion architectures that combine multiple environmental data streams into unified classifiers for early warning systems. Quantum-enhanced feature encoders and variational classifiers are benchmarked against XGBoost, random forests, and deep fusion networks. The goal is to establish whether quantum classifiers can extract cross-modal correlations that classical models miss.

Outputs

Open-source fusion library for multi-source environmental data; fused multi-modal environmental dataset; comprehensive quantum versus classical classifier benchmark with published methodology; paper on arXiv.

Multi-GPU Distributed Statevector Benchmarks

Cross-framework comparison of distributed statevector simulation across three quantum simulation frameworks at scale.

Timeline: Jun to Sep 2026

Summary

A rigorous comparison of distributed statevector simulation covering weak and strong scaling analysis, memory utilisation characterisation, and non-power-of-2 qubit mapping performance. Qubitry is benchmarked against Qiskit Aer GPU and PennyLane lightning.gpu under identical problem configurations. cuQuantum Ex API multi-process distribution on aarch64 was validated as a precondition, with 40-qubit simulations demonstrated across 128 GPUs.

Outputs

Multi-GPU scaling curves (1 to 32 GH200) for three frameworks; cross-framework comparison report with published methodology; weak and strong scaling analysis; paper on arXiv.