TY - GEN
T1 - Solving Drone Routing Problems with Quantum Computing
T2 - 2025 IEEE Congress on Evolutionary Computation, CEC 2025
AU - Osaba, Eneko
AU - Miranda-Rodriguez, Pablo
AU - Oikonomakis, Andreas
AU - Petrič, Matic
AU - Ruiz, Alejandra
AU - Bock, Sebastian
AU - Kourtis, Michail Alexandros
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - This paper presents a novel hybrid approach to solving real-world drone routing problems by leveraging the capabilities of quantum computing. The proposed method, coined Quantum for Drone Routing (Q4DR), integrates the two most prominent paradigms in the field: quantum gate-based computing, through the Eclipse Qrisp programming language; and quantum annealers, by means of D-Wave System's devices. The algorithm is divided into two different phases: an initial clustering phase executed using a Quantum Approximate Optimization Algorithm (QAOA), and a routing phase employing quantum annealers. The efficacy of Q4DR is demonstrated through three use cases of increasing complexity, each incorporating real-world constraints such as asymmetric costs, forbidden paths, and itinerant charging points. This research contributes to the growing body of work in quantum optimization, showcasing the practical applications of quantum computing in logistics and route planning.
AB - This paper presents a novel hybrid approach to solving real-world drone routing problems by leveraging the capabilities of quantum computing. The proposed method, coined Quantum for Drone Routing (Q4DR), integrates the two most prominent paradigms in the field: quantum gate-based computing, through the Eclipse Qrisp programming language; and quantum annealers, by means of D-Wave System's devices. The algorithm is divided into two different phases: an initial clustering phase executed using a Quantum Approximate Optimization Algorithm (QAOA), and a routing phase employing quantum annealers. The efficacy of Q4DR is demonstrated through three use cases of increasing complexity, each incorporating real-world constraints such as asymmetric costs, forbidden paths, and itinerant charging points. This research contributes to the growing body of work in quantum optimization, showcasing the practical applications of quantum computing in logistics and route planning.
KW - D-Wave
KW - Drone Routing
KW - Gate-based quantum computing
KW - Qrisp
KW - Quantum Annealing
KW - Quantum Computing
UR - https://www.scopus.com/pages/publications/105010515973
U2 - 10.1109/CEC65147.2025.11042978
DO - 10.1109/CEC65147.2025.11042978
M3 - Conference contribution
AN - SCOPUS:105010515973
T3 - 2025 IEEE Congress on Evolutionary Computation, CEC 2025
BT - 2025 IEEE Congress on Evolutionary Computation, CEC 2025
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 8 June 2025 through 12 June 2025
ER -