Project Lead: Finden
Sector: Cross-sectoral
Project Summary
The project tackles a growing bottleneck in materials science: modern diffraction and spectroscopy experiments generate data faster than conventional analysis methods can process. Traditional Rietveld fitting techniques are computationally intensive, scale poorly, and require significant manual intervention.
The team will develop advanced Hybrid Quantum Neural Networks (HQNNs) and a novel Hybrid Quantum Convolutional Neural Network (HQCNN), scaling from 6 to 10 qubits and enabling multi-pattern analysis within a single quantum circuit. The models will be deployed primarily on IBM Quantum Heron (133/156-qubit) and Eagle (127-qubit) superconducting quantum processors, with exploratory benchmarking on neutral-atom and ion-trap platforms available through the.
Expected outcomes include rapid extraction of crystallographic parameters from large experimental datasets, benchmarking against classical methods, demonstration of multi-pattern quantum processing, and evidence of a pathway toward quantum advantage for high-throughput materials characterisation in battery, catalyst, energy, pharmaceutical, and chemical manufacturing applications.
