We are saddened to learn of the passing of Professor Graham R. Nudd. Graham joined the department in 1984 and served as departmental chair for two decades. A few words have been shared by former colleagues to celebrate his life and career.

Congratulations to all of 2023-2024 MSc students graduating in January 2025.

The department would also like recognise the winners of the following MSc prizes:

Best Overall Data Analytics student: Pak Ho Gordon Sy

Best Overall Computer Science student: Olly Wortley

Best Data Analytics dissertation: Tianyi Huang

Best Computer Science dissertation: Olly Wortley

A paper co-authored by Matthias C. Caro has been featured on the cover of PRX Quantum. PRX Quantum is a premier journal for quantum information science and technology research. The work was a collaboration with Haimeng Zhao (Caltech & Tsinghua), Laura Lewis (Caltech & Google), Ishaan Kannan (Caltech), Yihui Quek (Harvard & MIT) and Hsin-Yuan Huang (Caltech, Google & MIT).

Characterizing a quantum system by learning its state or unitary evolution is a key tool in developing quantum devices, with applications in practical quantum machine learning, benchmarking, and error mitigation. However, in general, this task requires exponentially many resources. Prior knowledge is required to circumvent this exponential bottleneck. The paper pinpoints the complexity for learning states and unitaries that can be implemented by quantum circuits with a bounded number of gates, a broad setting that is topical for current quantum technologies. When measuring efficiency with respect to the number of accesses to the unknown quantum state or unitary, the paper presents and implements algorithms that are provably optimally efficient. Thereby, this work establishes the equivalence between the complexity of learning quantum states or unitaries and the complexity of creating them. However, it also shows that the data processing necessarily requires exponential computation time under reasonable cryptographic assumptions.