Data Science News
WATE Shortlisting
Two of our colleagues were recently shortlisted for the Warwick Award for Teaching ExcellenceLink opens in a new window.
Dr James ArchboldLink opens in a new window was shortlisted for the Faculty Staff Award, and Faye Chung Underwood for the Faculty PGR Who Teaches Award - a fantastic and well-deserved recognition.
As our Director of Student Experience, James plays a pivotal role in shaping an inclusive and supportive learning environment across the department. Alongside this leadership, he also makes an excellent contribution to our teaching provision. His engaging approach, enthusiasm, and genuine care for students are consistently reflected in the positive feedback he receives.
Faye joined us in September 2025 as a Tutor and has already made an excellent contribution to the department. She has played a key role in supporting the delivery of a wide range of modules, bringing both professionalism and enthusiasm to her teaching. Her approachable manner and commitment to student learning have been greatly appreciated by both students and colleagues, and she has quickly become a valued member of the team.
Many congratulations, James and Faye, on these achievements.
Latest academic promotion
We are pleased to announce that Dr Sayan Bhattacharya has been promoted to Professor, effective 1st June 2026.
Many congratulations to Sayan on this well-earned success!
Cloning vs Learning in Quantum Computing
In a recent work, Warwick DCS researchers Nikhil Bansal and Matthias C. Caro, together with Gaurav Mahajan (Yale University), explored a fundamental question that lies at the intersection of foundations of quantum theory and computer science.
The No-Cloning theorem says that it is impossible to perfectly clone quantum states. Even if we allow for approximate errors, quantum cloning of unstructured states remains as expensive as fully characterising them, as shown by R.F. Werner in 1998. In contrast, for reasons akin to No Free Lunch Theorems in machine learning, modern quantum learning theory considers structured classes of states and exploits their structure to learn them efficiently. This naturally leads to the question of whether cloning can be easier than learning for these structured classes of states.
In the new work, this question is answered negatively for stabilizer states. The authors proved that imposing this structural restriction does not separate cloning and learning. The authors prove this via a novel connection to sample amplificationLink opens in a new window, which was recently introduced to the learning theory literature by B. Axelrod, S. Garg, V. Sharan, and G. Valiant. The work constitutes concrete progress towards understanding whether cloning and learning are fundamentally equally hard.
This work was presented at QCTiP Link opens in a new windowin April 2026, and it will be presented at COLT in June/July 2026 and at TQC in September 2026.