Comparing Qubit and Qudit Encodings for EV Charging and Trip Assignment Problems

TL;DR

This paper compares qubit and qudit encodings in variational quantum algorithms for EV fleet management, demonstrating qudit encoding's advantages in resource efficiency and optimization performance.

Contribution

It introduces and evaluates a qudit encoding approach for combinatorial optimization, showing its benefits over traditional qubit encoding in resource use and solution quality.

Findings

Qudit encoding reduces Hilbert-space dimension exponentially.

Qudit-based QAOA achieves similar or better optimization results.

Qudit encoding requires less computational resources and runtime.

Abstract

Variational quantum algorithms have attracted attention for their potential to solve combinatorial optimization problems. We study how the choice of encoding affects the resource requirements and optimization behavior of a variational quantum optimization algorithm. In order to quantify these effects, realistically inspired constrained electric vehicle (EV) fleet management problems were considered. These problems couple determining the optimal EV battery 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 more directly. Both encodings guarantee the same feasible solution set, while the qudit encoding exponentially reduces the required Hilbert-space dimension. We solve many random instances of highly constrained uni- and bi-directional charging problems using qudit-based quantum approximate optimization algorithm (QAOA) and thoroughly evaluate the performance results. We find that the qudit encoding of customer trips achieves similar or better optimization performance at much reduced resource requirements and shorter simulation runtime. These results highlight qudit-native encodings as a practical route for integer and multi-valued scheduling problems in variational quantum optimization.