The objective of this group is to develop theory and design algorithms for quantum communication and computing, using AI and Information Theory, by applying tools from machine learning, applied probability; queueing theory and optimization. They are focusing on 2 main research areas – quantum networking, and quantum information theory.
Outputs to date include algorithms for optimal scheduling of quantum switches to distribute quantum entanglement in a networked setting. They have developed this using an algorithm based on reinforcement learning, applied probability and queuing theory. This development can optimise performance in quantum networks (crucial for scalability and enabling multiple quantum computers to work together). Optimal Scheduling in a Quantum Switch: Capacity and Throughput Optimality | 2025 ACM SIGMETRICS
Another line of work on quantum information theory focuses on quantum hypothesis testing, where one wishes to discriminate between two possible quantum systems based on quantum outputs – an exact characterization of the rate of learning was obtained.
