As the world celebrated the International Year of Quantum Science and Technology in 2025, the School of Computing and Data Science (CDS) at the University of Hong Kong continued to strengthen its role at the forefront of quantum research. Among the leaders in this rapidly evolving field is Professor Giulio Chiribella, who marks his 10th year at the University and leads the Quantum Information and Computation Initiative (QICI)—Hong Kong’s largest research team dedicated to quantum information theory and quantum computing.

As a core member of CDS, Prof. Chiribella’s work exemplifies the School’s commitment to advancing foundational science while bridging theory, computation, and real-world technological impact. Quantum mechanics underpins many modern innovations, from precision sensors to secure communication systems. Today, the global scientific community is racing to build large-scale quantum computers capable of solving problems beyond the reach of classical machines. Yet as quantum processors scale, so does their complexity: the description of a quantum system expands exponentially with each additional qubit, rendering full characterization infeasible using traditional techniques.

To address this challenge, Chiribella’s team is pioneering a new research direction that brings together quantum science and artificial intelligence. Building on two papers published in Nature Communications (2022, 2024), the team develops neural networks that learn to represent quantum systems directly from experimental data.

Rather than attempting to identify the entire quantum state, an impossible task for systems with hundreds of qubits, the neural networks learn compact representations that capture only the essential features of the system. This approach is similar to inferring the structure of a three-dimensional object from a handful of two-dimensional photographs: the AI reconstructs the missing information by identifying hidden patterns across the data.

One of the most striking findings is that the AI spontaneously groups similar quantum states together, revealing fundamental patterns that were not programmed into the network. The team further showed that a model trained on small systems can, in some cases, predict the behaviour of significantly larger quantum systems. This suggests that neural representations could become a powerful exploratory tool for studying quantum devices that are beyond the reach of conventional techniques.

The implications of this research are far reaching. AI driven representations could be used to:

• Verify the performance of emerging quantum computers

• Optimize control of quantum dynamics

• Mitigate noise in quantum communication and sensing

• Inform the design of large-scale quantum networks and architectures

Beyond its technical applications, the project raises deeper questions. Because neural networks learn without built-in assumptions, they may detect features of quantum systems that human researchers do not anticipate. This opens the possibility that AI could help identify new structures or behaviours in complex quantum regimes.

Through this interdisciplinary effort, combining quantum information theory, machine learning, and experimental physics, Prof. Chiribella and his team are advancing a new way of “seeing” quantum phenomena. Their work reinforces CDS’s role as a hub for transformative research that shapes the technologies of the future all the way from AI to quantum computing.