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

• URL: http://arxiv.org/abs/2605.10255v1
• Date: Mon, 11 May 2026 09:23:29 GMT
• Title: Comparing Qubit and Qudit Encodings for EV Charging and Trip Assignment Problems
• Abstract summary: We study how the choice of encoding affects the resource requirements and optimization behavior of a variational quantum optimization algorithm.<n>We compare a conventional binary (qubit) trip encoding with an integer (qudit) encoding that represents assignments more directly.<n>We find that the qudit encoding of customer trips achieves similar or better optimization performance at much reduced resource requirements and shorter simulation runtime.
• Score: 3.56904699288587
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Scalable Variational Quantum Optimization via Pauli Correlation Encoding: Application to Large-Scale Power Demand Portfolio Optimization [14.022615381217628]
  We introduce a scalable variational framework based on Pauli correlation encoding (PCE) and apply it to electric power demand portfolio optimization.<n> Numerical simulations demonstrate near-optimal performance across problem sizes ranging from $m$=18 to 10,296, with normalized cost gaps on the order of $10-4$ relative to solutions with certified optimality.
  arXiv  Detail & Related papers  (2026-07-27T17:55:23Z)
• Direct phase encoding in QAOA: Describing combinatorial optimization problems through binary decision variables [0.7015624626359264]
  We show a more qubit-efficient circuit construction for optimization problems by the example of the Travelingperson Sales Problem (TSP)<n>Removing certain redundancies, the number of required qubits can be reduced by a linear factor compared to the aforementioned conventional encoding.<n>Our experiments show that for small instances results are just as accurate using our proposed encoding, whereas the number of required classical iterations increases only slightly.
  arXiv  Detail & Related papers  (2024-12-10T12:12:34Z)
• Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
  Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems. In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems. We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
  arXiv  Detail & Related papers  (2024-04-17T11:00:12Z)
• Efficient Encodings of the Travelling Salesperson Problem for Variational Quantum Algorithms [0.0]
  We investigate different encodings for the Travelling Salesperson Problem. We find evidence that the permutation encoding can yield good results since it does not suffer from feasibility issues.
  arXiv  Detail & Related papers  (2024-04-08T12:30:07Z)
• Guess What Quantum Computing Can Do for Test Case Optimization [43.89456212504871]
  In the near term, quantum approximate optimization algorithms (QAOAs) hold great potential to solve optimization problems. We present the first effort to formulate a software test case optimization problem as a QAOA problem and solve it on quantum computer simulators. To solve bigger test optimization problems that require many qubits, which are unavailable these days, we integrate a problem decomposition strategy with the QAOA.
  arXiv  Detail & Related papers  (2023-12-24T21:25:31Z)
• Qubit efficient quantum algorithms for the vehicle routing problem on NISQ processors [48.68474702382697]
  Vehicle routing problem with time windows (VRPTW) is a common optimization problem faced within the logistics industry. In this work, we explore the use of a previously-introduced qubit encoding scheme to reduce the number of binary variables.
  arXiv  Detail & Related papers  (2023-06-14T13:44:35Z)
• Prog-QAOA: Framework for resource-efficient quantum optimization through classical programs [0.0]
  Current quantum optimization algorithms require representing the original problem as a binary optimization problem, which is then converted into an equivalent cost Hamiltonian suitable for the quantum device.<n>We propose to design classical programs for computing the objective function and certifying the constraints, and later compile them to quantum circuits.<n>We exploit this idea for optimization tasks like the Travelling Salesman Problem and Max-$K$-Cut and obtain circuits that are near-optimal with respect to all relevant cost measures.
  arXiv  Detail & Related papers  (2022-09-07T18:01:01Z)
• Quantum approximate optimization algorithm for qudit systems [0.0]
  We discuss the quantum approximate optimization algorithm (QAOA) for qudit systems. We illustrate how the QAOA can be used to formulate a variety of integer optimization problems. We present numerical results for a charging optimization problem mapped onto a max-$k$-graph coloring problem.
  arXiv  Detail & Related papers  (2022-04-01T10:37:57Z)
• Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
  We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions. Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
  arXiv  Detail & Related papers  (2021-06-24T20:16:02Z)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)
• Space-efficient binary optimization for variational computing [68.8204255655161]
  We show that it is possible to greatly reduce the number of qubits needed for the Traveling Salesman Problem. We also propose encoding schemes which smoothly interpolate between the qubit-efficient and the circuit depth-efficient models.
  arXiv  Detail & Related papers  (2020-09-15T18:17:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.