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Journal of Communications and Networks

Solving optimal electric vehicle charging station placement problem using digital quantum annealing

Chia‐Ho Ou, Chung-Chieh Cheng, Chih‐Yu Chen, Krischonme N. Bhumkittipich, Sillawat Romphochai

Published Aug 1, 2025• 8 citations
Publisher

Abstract

It is imperative to reduce carbon dioxide emissions and fossil fuel consumption to ensure the Earth's sustainable future and mitigate negative impacts like extreme climate changes caused by greenhouse gases. Electric vehicles (EVs) play a pivotal role in this context, where the convenience of charging stations is crucial in influencing consumer choices. This paper formulates the placement of charging stations as an optimization problem, considering factors like station density, accessibility, coverage, and regional cost variations, to minimize total construction costs. The problem is mapped onto a quadratic unconstrained binary optimization (QUBO) model. The QUBO can efficiently represent and solve complex combinatorial optimization problems, making it suitable for quantum and advanced classical algorithms. Utilizing digital quantum annealing, the optimal or near-optimal solutions are efficiently identified. The performance of the proposed approach is compared with classical algorithms, including mixed linear programming and simulated annealing, demonstrating significant advancements of digital annealing.

DOI
10.23919/jcn.2025.000048

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The infrastructure development of electric vehicle charging stations (EVCS) is critical to the integration of electrical vehicles (EVs) into transportation systems, which requires significant investment and has long-term impact on the adoption of EVs. In this paper, a mathematical model is developed to identify the optimal placement of EVCS by utilizing a novel quantum annealing (QA) algorithm and quantum computation (QC). The objective of the optimization model is to determine the locations of EVCS that maximize their service quality for EV users. The model is validated using a real-world case study and solved using commercially available quantum computers from D-Wave. The case study shows that the QA algorithm can find the optimal placement of EVCS within seconds. The quality of the solutions obtained using QC is not sensitive to the shape or size of the area where EVCS are to be deployed.
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This paper proposes a novel dispatch formulation for micro-mobility vehicles using a quantum annealing (QA). In recent years, QA has gained increasing attention as a high-performance solver for combinatorial optimization problems. Meanwhile, micro-mobility services have been rapidly developed as a promising means of realizing efficient and sustainable urban transportation. In this study, the dispatch problem for such micro-mobility services is formulated as a quadratic unconstrained binary optimization (QUBO) problem, enabling efficient solving through QA. Furthermore, the proposed formulation incorporates historical usage data to enhance operational efficiency. Specifically, customer arrival frequencies and destination distributions are modeled into the QUBO formulation through a Bayesian approach, which guides the allocation of vacant vehicles to designated stations for waiting and charging. Simulation experiments are conducted to evaluate the effectiveness of the proposed method, with comparisons to conventional formulations such as the vehicle routing problem. Additionally, the performance of QA is compared with that of classical solvers to reveal its potential advantages for the proposed dispatch formulation. The effect of reverse annealing on improving solution quality is also investigated.
This paper proposes a novel dispatch formulation for micro-mobility vehicles using a Quantum Annealer (QA). In recent years, QA has gained increasing attention as a high-performance solver for combinatorial optimization problems. Meanwhile, micro-mobility services have been rapidly developed as a promising means of realizing efficient and sustainable urban transportation. In this study, the dispatch problem for such micro-mobility services is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem, enabling efficient solving through QA. Furthermore, the proposed formulation incorporates historical usage data to enhance operational efficiency. Specifically, customer arrival frequencies and destination distributions are modeled into the QUBO formulation through a Bayesian approach, which guides the allocation of vacant vehicles to designated stations for waiting and charging. Simulation experiments are conducted to evaluate the effectiveness of the proposed method, with comparisons to conventional formulations such as the vehicle routing problem. Additionally, the performance of QA is compared with that of classical solvers to reveal its potential advantages for the proposed dispatch formulation. The effect of reverse annealing on improving solution quality is also investigated.
In this research paper, we employ D-Wave’s quantum annealer to address the NP-hard problem of optimizing new metro station locations while considering both points of interest and existing stations. By formulating the problem as Quadratic Unconstrained Binary Optimization (QUBO) and Binary Quadratic Model (BQM), our approach harnesses the power of quantum computing to yield superior solutions compared to classical methods. This research has the potential to revolutionize urban transportation planning by efficiently balancing accessibility to points of interest and minimizing congestion around existing stations, offering a compelling solution to a complex urban development challenge. Notably, we emphasize the NP-hard nature of this problem, underlining its computational complexity and the significance of our quantum computing approach.
Siting a limited number of fast-charging hubs so that the greatest share of urban charging demand falls within reach is a discrete, NP-hard facility-location task that grows combinatorially with the number of candidate parcels. We recast this task for gate-model quantum hardware. Demand intensity and candidate parcels are extracted from a public Government of NCT of Delhi charging-station record of 2,270 georeferenced points, a demand-weighted maximumcoverage objective with a cardinality constraint is mapped to a Quadratic Unconstrained Binary Optimization (QUBO) model, and the QUBO is expressed as an Ising cost Hamiltonian solved by the Quantum Approximate Optimization Algorithm (QAOA). On a twelve-parcel instance selecting five hubs, exact enumeration places the optimum at 53.2% covered demand; QAOA recovers an approximation ratio that rises from 0.67 at depth p=1 to 0.93 at p=3, while a depth-four circuit regresses to 0.83 under a fixed optimizer budget, illustrating the trainability cost of added layers. At this NISQ-tractable size, greedy and simulated-annealing baselines both reach the exact optimum, so the quantum contribution is algorithmic rather than a wall-clock advantage; an explicit size sweep shows the classical search space and exact-solve effort growing as 2^N, which is where deeper quantum circuits are expected to matter. The study is reported transparently, including the negative parity results.