Francesco Tudisco

Associate Professor (Reader) in Machine Learning

School of Mathematics, The University of Edinburgh
The Maxwell Institute for Mathematical Sciences
School of Mathematics, Gran Sasso Science Institute JCMB, King’s Buildings, Edinburgh EH93FD UK
email: f dot tudisco at

Testing Quantum and Simulated Annealers on the Drone Delivery Packing Problem

Sara Tarquini, Daniele Dragoni, Matteo Vandelli, Francesco Tudisco,
preprint, (2024)


Using drones to perform human-related tasks can play a key role in various fields, such as defense, disaster response, agriculture, healthcare, and many others. The drone delivery packing problem (DDPP) arises in the context of logistics in response to an increasing demand in the delivery process along with the necessity of lowering human intervention. The DDPP is usually formulated as a combinatorial optimization problem, aiming to minimize drone usage with specific battery constraints while ensuring timely consistent deliveries with fixed locations and energy budget. In this work, we propose two alternative formulations of the DDPP as a quadratic unconstrained binary optimization (QUBO) problem, in order to test the performance of classical and quantum annealing (QA) approaches. We perform extensive experiments showing the advantages as well as the limitations of quantum annealers for this optimization problem, as compared to simulated annealing (SA) and classical state-of-the-art commercial tools for global optimization.

Please cite this paper as:

  title={Testing Quantum and Simulated Annealers on the Drone Delivery Packing Proble},
  author={Tarquini, Sara and Dragoni, Daniele  and Vandelli, Matteo and Tudisco, Francesco},

Links: arxiv

Keywords: quantum computing quantum annealing combinatorial optimization