Dr Xing Liang, Kingston University London

The challenge

Clean and sustainable energy generation remains one of the major challenges facing society. As sensors, small electronics and off-grid technologies become more widely used, there is a growing need for low-cost ways to generate power at small scales. One promising opportunity lies in the energy released by tiny bubbles that form and collapse when liquids move quickly through small channels. This effect could also potentially be applied in water treatment, chemical processing and drug delivery. However, designing devices that can reliably control and capture this energy is difficult because the behaviour of these bubbles is complex and hard to predict. 

The research

Dr Xing Liang will tackle this challenge by developing a hybrid classical-quantum machine learning approach, which combines AI with quantum computing to improve the way these small energy-harvesting devices are designed.  

Her research will develop advanced quantum AI-driven methods that can learn from device features, such as the shape of small channels, and predict how well different designs will perform. This could reduce the need for repeated trial-and-error testing, lower development costs and make it faster to create more efficient devices. 

The researcher 

Dr Xing Liang is a Senior Lecturer at Kingston University London and the Founder and Head of the Quantum Artificial Intelligent (QAI) Research Group. Her research focuses on developing and applying hybrid quantum–classical machine learning, quantum-inspired AI algorithms, and classical AI to address complex real-world challenges, with ongoing projects spanning sustainable energy, finance, healthcare, and cybersecurity.