ÌÇÐÄTV

co-authored by Matthias C. Caro has been of 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.

We are happy to announce that an academic from our department, , is among the winners of . According to the European Research Council: "These grants, totalling €678 million, aim to support outstanding scientists and scholars as they establish their independent research teams and develop their most promising scientific ideas. The funding is provided through the EU's Horizon Europe programme."

has been awarded a €2million ERC Consolidator grant for a 5-year project entitled "Towards a Dynamic Algorithms Centric Theory of Linear Programming" (DYNALP). The project aims to build a new theory exploring the interplay between two key concepts, Linear Programming and Dynamic Algorithms, which, in turn, will pave the way towards attacking outstanding open questions in the field of Theoretical Computer Science.

In the 2024 round, this was the only project from the United Kingdom that was awarded an ERC Consolidator Grant in Computer Science and Informatics (PE6 panel). The contains more information about the ERC funding programme.

We are delighted to announce that , a PhD student at the Theory and Foundations research division, has received a highly competitive for his work on designing clustering algorithms for dynamic datasets. The Fellowship comes in the form of an unrestricted gift from Google, of 60,000 USD per year, for up to two years. Under the category of "Algorithms and Theory", besides Martin (from University of Cambridge and ETH Zurich) received a Google PhD Fellowship this year. Many congratulations to Martin for this achievement!

co-authored by Arpan MukhopadhyayLink opens in a new window has received the Best Paper Award at . Mobihoc is a premier international conference on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing. The other authors in the paper are Samira Ghanbarian (uWaterloo), Ravi R. Mazumdar (uWaterloo), and Fabrice Guillemin (Orange Labs, France).

The paper addresses the problem of optimally allocating processors to parallelisable tasks having arbitrary concave speed-up functions. In general, determining the optimal number of processors to allocate to each task in an online fashion is a hard problem since allocating too many processors to one job will make those processors unavailable to other jobs whereas allocating too few processors will result in a small speed-up for the job. The paper proposes a simple randomised algorithm for determining the optimal number of processors to allocate to each job without requiring preemption (or repacking). It shows that the proposed algorithm is asymptotically optimal as the number of processors becomes large (which is often the case in modern clouds) and is also robust to variations in the job size distribution. This is the first time such an algorithm has been found in the literature.

Neha Rino, a PhD student in the Theory and Foundations group in the Department of Computer Science and a member of the Cyber Security group at WMG, has won an at FORMATS 2024.

The Oded Maler award is a distinction presented for the best paper of the International Conference on Formal Modeling and Analysis of Timed Systems (FORMATS). of the conference was held in September in Calgary, Canada, jointly with QEST (International Conference on Quantitative Evaluation of SysTems) as a common research forum dedicated to quantitative modelling, analysis, and verification.

Neha's paper, "", is co-authored with Mohammed Foughali and Eugene Asarin, both from and in Paris, France, where Neha completed the Master's degree (ENS Paris-Saclay) prior to joining ÌÇÐÄTV.

Neha's paper contributes to the research framework of quantitative monitoring, which is the analysis of individual executions of systems which yields numerical output (real numbers), rather than binary yes/no. The paper formulates and solves, by an efficient algorithm, a new problem of this kind: computing a real number that characterises to which extent the given execution of a satisfies its specification expressed in (STL).

Eight papers authored by Computer Science researchers from ÌÇÐÄTV have been accepted for publication at the , the leading international venue for machine learning research, which will be held on 10-15 December 2024 in Vancouver, British Columbia, Canada:

• Generating Origin-Destination Matrices in Neural Spatial Interaction Models, by Ioannis Zachos, Mark Girolami, and Theodoros Damoulas
• Interventionally Consistent Surrogates for Complex Simulation Models, by Joel Dyer, Nicholas Bishop, Yorgos Felekis, Fabio Massimo Zennaro, Ani Calinescu, Theodoros Damoulas, and Michael Wooldridge
• Learning the Expected Core of Strictly Convex Stochastic Cooperative Games, by Phuong Nam Tran, The Anh Ta, Shuqing Shi, Debmalya Mandal, Yali Du, and Long Tran-Thanh
• Physics-Informed Variational State-Space Gaussian Processes, by Oliver Hamelijnck, Arno Solin, and Theodoros Damoulas
• SARAD: Spatial Association-Aware Anomaly Detection and Diagnosis for Multivariate Time Series, by Zhihao Dai, Ligang He, Shuanghua Yang, and Matthew Leeke
• Symmetric Linear Bandits with Hidden Symmetry, by Phuong Nam Tran, The Anh Ta, Debmalya Mandal, and Long Tran-Thanh
• The Effectiveness of Surprisingly Popular Voting with Partial Preferences, by Hadi Hosseini, Debmalya Mandal, and Amrit Puhan
• What makes unlearning hard and what to do about it, by Kairan Zhao, Meghdad Kurmanji, George-Octavian Bărbulescu, Eleni Triantafillou, and Peter Triantafillou

We are seeking PhD candidates in the topic of Multiagent Systems and related areas, with particular emphasis on one or more of: computational social choice, algorithmic game theory, multiagent learning, and social and economic networks. The multiagent systems researchers at University of ÌÇÐÄTV include , , , Long Tran-Thanh, and .

The expected starting date is October 2025 or as soon as possible thereafter. The deadline for our internal application round is 1 November 2024. To apply, please fill out the (which will ask you to upload a CV and a letter of motivation). We aim to have interviews between November 11th and 22nd, 2024. Top-ranked candidates will be put forward for a fully funded position through the Computer Science Centre for Doctoral Training and Research (CDT) by January 15th 2025.