Drone delivery packing problem on a neutral-atom quantum computer

TL;DR

This paper presents a hybrid quantum-classical approach using neutral-atom quantum computers to solve the drone delivery packing problem by reformulating it as a graph-partitioning task, demonstrating effectiveness through simulations and hardware experiments.

Contribution

It introduces a novel quantum-assisted method for drone delivery scheduling that leverages neutral-atom quantum processors to efficiently find optimal solutions.

Findings

Successfully implemented on Pasqal's Fresnel QPU with up to 100 atoms.

Demonstrated the approach's effectiveness through numerical emulations.

Showed potential for solving complex scheduling problems with quantum hardware.

Abstract

Quantum architectures based on neutral atoms have gained significant attention in recent years as specialized computational machines due to their ability to directly encode the independent set constraint on graphs, exploiting the Rydberg blockade mechanism. In this work, we address the Drone Delivery Packing Problem via a hybrid quantum-classical framework leveraging a neutral-atom quantum processing unit (QPU). We reformulate the optimization task as a graph-partitioning problem based on the independent sets (ISs) of a scheduling graph that encodes delivery incompatibilities. Each partition corresponds to deliveries assigned to a single drone, with the objective of minimizing the total number of partitions. While the ISs represent time-feasible schedules, battery-duration constraints are enforced through a classical post-processing routine. This methodology enables the recovery of optimal delivery schedules, provided a sufficient number of samples is collected from the QPU to resolve the solution space. We benchmark the hybrid workflow through numerical emulations and demonstrate its effectiveness on Pasqal's Fresnel QPU, reporting hardware experiments with configurations of up to 100 atoms.