Associate Professor (Reader) in Machine Learning
School of Mathematics, The University of Edinburgh
The Maxwell Institute for Mathematical Sciences
Alan Turing Institute | Bayes Centre | Gen AI Lab
School of Mathematics, Gran Sasso Science Institute
email: f dot tudisco at ed.ac.uk
Excited to announce that our paper Drone delivery packing problem on a neutral-atom quantum computer has been accepted for publication in Advanced Quantum Technologies. Great work by Sara Tarquini, in collaboration with Matteo Vandelli, Francesco Ferrari, and Daniele Dragoni!
The paper tackles the Drone Delivery Packing Problem with a hybrid quantum-classical framework running on a neutral-atom quantum processing unit. We recast the task as a graph-partitioning problem over the independent sets of a scheduling graph that encodes delivery incompatibilities, letting the Rydberg blockade enforce the independent-set constraint directly on hardware, while battery-duration limits are handled in a classical post-processing step. Beyond numerical emulations, we run experiments on Pasqal’s Fresnel QPU with configurations of up to 100 atoms.