Quantum Computing Enhances AI for Chaotic Systems and Drug Discovery
At University College London’s Centre for Computational Science, researchers have introduced Quantum-Informed Machine Learning (QIML) to improve AI modeling of chaotic phenomena such as turbulence and weather patterns. The research team, which includes Maida Wang, Dr. Xiao Xue, Mingyang Gao, and Prof. Peter Coveney, utilized a quantum computer from IQM Quantum Computers to develop a 'Quantum Prior' (Q-Prior), resulting in enhancements of predictive accuracy by as much as 17% and fidelity by up to 29% for equations like the Kuramoto-Sivashinsky. Prof. Coveney emphasized the practical benefits of quantum technology. On March 16, 2026, UCL unveiled a hybrid approach for simulating GPCRs at NVIDIA's GTC conference, followed by the UK government’s announcement of a £2 billion investment in quantum technologies on March 17, 2026.
Key facts
- Quantum-Informed Machine Learning (QIML) uses quantum computers to improve AI modeling of chaotic systems.
- The method was developed by a UCL team including Maida Wang, Dr Xiao Xue, Mingyang Gao, and Prof Peter Coveney.
- A quantum computer from IQM Quantum Computers generates a Quantum Prior (Q-Prior) to guide AI predictions.
- QIML boosted predictive accuracy by up to 17% and fidelity by up to 29% on chaotic systems.
- The research is published in the journal Science Advances.
- On March 16, 2026, at NVIDIA's GTC conference in San Jose, a hybrid approach simulated GPCRs using quantum computing.
- The Euro-Q-Exa, a 54-qubit quantum processor at the Leibniz Supercomputing Center in Germany, was used for molecular simulation.
- The UK government announced a £2 billion investment in quantum technology on March 17, 2026.
Entities
Artists
- Maida Wang
- Dr Xiao Xue
- Mingyang Gao
- Prof Peter Coveney
- Prof John Morton
Institutions
- Centre for Computational Science
- University College London (UCL)
- IQM Quantum Computers
- Science Advances
- NVIDIA
- GTC conference
- Leibniz Supercomputing Center (LRZ)
- UCL Quantum Science and Technology Institute
- Science Museum Blog
Locations
- London
- United Kingdom
- San Jose
- United States
- Germany