Quantum Algorithms for Drone Mission Planning
Ethan Davies, Pranav Kalidindi

TL;DR
This paper explores the application of near-term quantum algorithms to solve complex drone mission planning problems, formulating them as QUBO problems and testing solutions on quantum annealers and variational algorithms.
Contribution
It introduces a versatile QUBO formulation for drone routing problems and compares quantum annealing, QAOA, and VQE methods against classical solvers.
Findings
Quantum annealers can find solutions comparable to classical solvers.
QAOA and VQE show promise but require further optimization.
Formulation scales clearly with problem size and constraints.
Abstract
Mission planning often involves optimising the use of ISR (Intelligence, Surveillance and Reconnaissance) assets in order to achieve a set of mission objectives within allowed parameters subject to constraints. The missions of interest here, involve routing multiple UAVs visiting multiple targets, utilising sensors to capture data relating to each target. Finding such solutions is often an NP-Hard problem and cannot be solved efficiently on classical computers. Furthermore, during the mission new constraints and objectives may arise, requiring a new solution to be computed within a short time period. To achieve this we investigate near term quantum algorithms that have the potential to offer speed-ups against current classical methods. We demonstrate how a large family of these problems can be formulated as a Mixed Integer Linear Program (MILP) and then converted to a Quadratic…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture
MethodsSparse Evolutionary Training
