@inproceedings {pub6670,
	title = {Comparing Qubit and Qudit Encoding for EV Charging and Trip Assignment Problems},
	author = {Linus Ekstrom AND Hao Wang AND Sebastian Schmitt},
	year = {2026},
	month = {July},
	abstract = {Variational quantum algorithms have garnered attention for their promise in solving combinatorial optimization problems. 
We study how the encoding choice impacts resource requirements and optimization behavior of a variational quantum optimization algorithms.
We consider a realistic constrained electric vehicle (EV) fleet management problem that couples determining the optimal bidirectional charging schedule with assigning EVs to trips requested by customers. 
We compare a conventional binary (qubit) trip encoding with an integer (qudit) encoding that represents assignments directly. 
Both encoding guarantee the same feasible solution set. 
The more direct qudit encoding exponentially reduces the Hilbert-space dimension and lowers simulation runtime, while achieving slightly better performance in shot-based state-vector benchmarks. 
These results highlight qudit-native encodings as a practical route for integer and multi-valued scheduling problems in variational quantum optimization.},
	publisher = {ACM},
	booktitle = {The Genetic and Evolutionary Computation Conference (GECCO) }
}