2 citations · 4 across the 5 of their papers we have counts for
5 papers
Graph Transformers and Stabilized Reinforcement Learning for Large-Scale Dynamic Routing Modulation and Spectrum Allocation in Elastic Optical Networks
Michael Doherty, Alejandra Beghelli, Laura Toni
Reinforcement learning (RL) has been widely applied to dynamic routing, modulation and spectrum assignment (RMSA) in optical networks, yet no prior work has trained a transformer m…
Fast stabilizer state preparation via AI-optimized graph decimation
Michael Doherty, Matteo Puviani, Jasmine Brewer +4
We propose a general method for preparing stabilizer states with reduced two-qubit gate count and depth compared to the state of the art. The method starts from a graph state repre…
Reinforcement Learning with Graph Attention for Routing and Wavelength Assignment with Lightpath Reuse
Michael Doherty, Alejandra Beghelli
Many works have investigated reinforcement learning (RL) for routing and spectrum assignment on flex-grid networks but only one work to date has examined RL for fixed-grid with fle…
Reinforcement Learning for Dynamic Resource Allocation in Optical Networks: Hype or Hope?
Michael Doherty, Robin Matzner, Rasoul Sadeghi +2
The application of reinforcement learning (RL) to dynamic resource allocation in optical networks has been the focus of intense research activity in recent years, with almost 100 p…
Topology Bench: Systematic Graph Based Benchmarking for Core Optical Networks
Robin Matzner, Akanksha Ahuja, Rasoul Sadeghi +4
Topology Bench is a comprehensive topology dataset designed to accelerate benchmarking studies in optical networks. The dataset, focusing on core optical networks, comprises public…