Curator's Take
This article tackles a crucial bottleneck in quantum optimization algorithms by demonstrating that qudit (multi-level quantum states) encodings can dramatically reduce resource requirements compared to traditional qubit approaches. By testing on realistic electric vehicle fleet management problems, the researchers show that qudits can represent complex scheduling constraints more naturally while using exponentially less quantum memory and achieving similar optimization performance. This work is particularly significant because it addresses practical near-term quantum computing limitations - showing how clever encoding strategies can make complex real-world problems more tractable on today's noisy quantum devices. The findings suggest that moving beyond binary qubits to multi-level quantum systems could be a game-changer for quantum advantage in logistics and scheduling applications.
— Mark Eatherly
Summary
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.