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arXiv:2608.00524

Cooperative Platoon Routing and Dispatching via Edge-Assisted Hybrid Quantum Optimization

Talha Azfar, Ruimin Ke

quant-phcs.ETSubmitted Aug 1, 2026• Announced Aug 4, 2026
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Abstract

Cooperative platooning can reduce the energy use of Connected and Autonomous Vehicle (CAV) fleets, but the routing problem becomes difficult when vehicles must meet on the same road segments at compatible times while moving through unstable urban traffic. This paper develops an edge-assisted, closed-loop evaluation pipeline for platooning-aware vehicle routing. Roadside Units estimate local traffic kinematics from video, classify segment-level flow stability, and activate platooning rewards only on road segments where close-gap coordination is physically appropriate. The resulting multi-vehicle routing problem is written directly as a Quadratic Unconstrained Binary Optimization (QUBO) model, so pairwise platooning interactions are represented as native quadratic Ising terms instead of requiring auxiliary MILP linearization variables. We evaluate the framework using a 24-hour microscopic SUMO simulation of Troy, NY, together with localized IBM Quantum hardware benchmarks. The SUMO study shows an $18.5\%$ reduction in fleet tractive-energy demand relative to a non-cooperative baseline. On 25-active-qubit benchmark instances executed on $\texttt{ibm_boston}$, Linear-Chain QAOA reduces two-qubit CNOT depth by $66.7\%$ compared with dense QAOA and samples the exact classical ground state with $P_{\text{feas}} = 38.6\%$ and $P_{\text{opt}} = 14.2\%$ at $p=2$. These results suggest that edge perception and shallow quantum optimization can work together as a useful component of closed-loop CAV platoon dispatching.

Related papers

Quantum-Classical Hybrid Cloud Workflows for Combinatorial Supply-Chain and Fleet Optimization

Sonu Kumar Singh

Abstract: Global logistics networks, intercontinental air-freight consolidators, and maritime energy fleets continuously solve large-scale Mixed-Integer Linear Programs (MILPs) such as the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) and multi-vessel Maritime Inventory Routing Problems (MIRP). When weather disruptions, port congestion, or geopolitical airspace closures invalidate baseline schedules, classical branch-and-cut solvers (Gurobi, CPLEX) experience exponential worst-case tree growth on subproblems exceeding 2,000 binary decision variables, often failing to converge within strict 5-to-15-minute operational re-dispatch windows. Conversely, current Noisy Intermediate-Scale Quantum (NISQ) processors remain limited to 100-to-1,000 physical qubits with finite two-qubit gate fidelity ($99.2\%-99.8\%$), preventing direct encoding of full enterprise logistics instances. This paper presents a production-grade Quantum-Classical Hybrid Cloud Architecture that bridges enterprise Kubernetes clusters with co-located quantum processing units (QPUs) via AWS Braket Hybrid Jobs and IBM Quantum Qiskit Runtime primitives. Our framework applies Benders and Dantzig-Wolfe column generation on classical cloud HPC nodes to decompose a 25,000-variable global fleet MILP into a continuous master problem and hard combinatorial pricing/routing subproblems of 60 to 140 binary variables. These subproblems are transformed into Quadratic Unconstrained Binary Optimization (QUBO) and Ising Hamiltonians, warm-started from classical linear programming (LP) relaxations (WS-QAOA), compiled with dynamical decoupling and Zero-Noise Extrapolation (ZNE), and executed inside co-located QPU-CPU sessions while a GPU-accelerated Simulated Bifurcation (SB) digital annealer runs in parallel as a deterministic SLA fallback. Evaluated on disruption recovery workloads modeled on ExxonMobil maritime LNG routing and DHL Global Forwarding air-cargo container loading, our hybrid WS-QAOA + Column Generation workflow improves optimality gap closure at a 10-minute SLA cutoff from 6.84% (classical branch-and-bound alone) to 1.42%, reduces emergency re-routing compute wall-clock time by 61.5%, and cuts fleet fuel and charter penalty costs by 4.7%. Research Domain & Specialization Track Master of Science in Information Technology (MSIT) — Quantum Cloud Computing, Hybrid Orchestration, and Operations Research. Core Technical Highlights Empirical Production Case Studies: Rigorous architectural blueprints and telemetry evaluation across enterprise cloud deployments. Four-Tier Reference Architecture: Formal specification from edge ingress to distributed hardware execution pools. Mathematical Formulation: Formally derived algorithmic and optimization models with closed-loop verification. Multi-Modal Benchmarking: Comparative empirical baselines under fault-injection and synthetic workload scaling. Author Information Author: Sonu Kumar SinghRole: Senior Consultant Affiliation: Independent Researcher Publication Status: Preprint / Working Paper (MSIT AI & Cloud Systems Research Series) Reproducibility & Open Science All included architecture diagrams, telemetry schemas, and benchmark curves are open-access under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

•Oct 20, 2026•0 citations

Quantum-Accelerated Feature Selection for Edge-Enabled Perception in Connected and Autonomous Vehicles

Vikas Hassija, Avishikta Bhattacharjee, Aradhya Chakrabarti +3 more

The growing complexity and volume of traffic and sensory data in Connected and Autonomous Vehicles (CAVs) make it crucial to develop more effective methods for selecting and extracting features from high-dimensional images and videos. This paper proposes a novel quantum-accelerated feature selection framework utilising the Variational Quantum Eigensolver (VQE) to enhance perception tasks in CAV environments. By decomposing high-dimensional traffic images into compact patches encoded as quantum Hamiltonians, the proposed design aims to support Intelligent Transportation Systems (ITS) tasks such as object detection, vehicle classification, and traffic monitoring in CAVs.The quantum hybrid method serves as a pipeline for extracting selective features that have been optimised using gradient-aware parameter pruning and conditional principal component analysis (PCA). The implementation of this quantum hybrid approach facilitates addressing computational challenges at the edge in embedded vehicular systems, reducing data dimensionality while preserving key discriminative spatial patterns necessary for object detection and traffic monitoring. Although the quantum method currently underperforms in certain clustering and separability metrics, it demonstrates potential as a viable alternative for advanced perception in CAVs.This work lays a foundation for integrating quantum computing into edge-based autonomous vehicle perception, highlighting both opportunities and challenges with near-term quantum hardware.

IEEE Access•Jan 1, 2025•1 citation

Enhancing vehicle routing problem through a qubit-efficient Symmetric Arc Reduction using Quantum Approximate Optimization Algorithm

Jayaraja A, Senthil Kumaran U

Introduction Finding the most efficient routes for a fleet of delivery vehicles remains a critical and costly challenge for nearly any logistics business. This core idea is known as the Vehicle Routing Problem (VRP). The primary goal is to minimize operational costs, such as the total travel distance or the number of trucks deployed, while ensuring all the customers are served. Because VRP is NP-hard, finding a perfect solution is often impossible, forcing industries to rely on classical approximate algorithms. However, quantum variational algorithms offer a new paradigm by iteratively optimizing parameters to navigate the solution space, presenting a new and promising avenue for finding better and faster solutions. Methods In this paper, we introduce Symmetric Arc Reduction in Quantum Approximate Optimization Algorithm (SAR-QAOA), which systematically eliminates redundant symmetric routing variables in the binary integer programming formulation prior to the Quadratic Unconstrained Binary Optimization (QUBO) formulation. The proposed SAR-QAOA encodes edge-based VRP while reducing the qubit requirement from n ( n −1) to n ( n −1)/2, thereby improving the resource efficiency of the QUBO formulation by prioritizing regions of the solution space that are more likely to contain high-quality solutions; SAR enhances both search accuracy and computational efficiency. Results Experimental results on noiseless quantum simulators validate the feasibility of the proposed formulation and its integration with the QAOA framework, providing a promising foundation for future investigations on larger routing problems and evolving quantum hardware.

Frontiers in Artificial Intelligence•Oct 1, 2026•0 citations

Quantum Optimization Pipeline for the Capacitated Vehicle Routing Problem (CVRP)

Ninad Hirani

Quantum Optimization Pipeline for the Capacitated Vehicle Routing Problem (CVRP) This repository presents a research-oriented implementation of classical and quantum optimization techniques for solving the Capacitated Vehicle Routing Problem (CVRP). The project serves as an educational and experimental framework for studying hybrid quantum-classical optimization methods and benchmarking their performance on combinatorial optimization problems. The implementation includes: Classical CVRP optimization using Google OR-Tools Mathematical formulation of the routing problem Transformation of the optimization problem into a Quadratic Unconstrained Binary Optimization (QUBO) model Conversion of the QUBO formulation into an Ising Hamiltonian Implementation of the Quantum Approximate Optimization Algorithm (QAOA) using Qiskit Classical versus quantum performance comparison Route visualization and benchmarking Modular Python code designed for experimentation and future extensions The project is intended as a foundation for exploring applications of Quantum Computing, Operations Research, and Hybrid Quantum-Classical Optimization in real-world routing and logistics problems. It provides a reproducible environment for understanding optimization workflows, evaluating quantum algorithms, and extending the framework toward more complex scenarios such as dynamic vehicle routing and emergency ambulance dispatch. Keywords: Quantum Computing, Quantum Optimization, QAOA, QUBO, Ising Model, Capacitated Vehicle Routing Problem (CVRP), Operations Research, OR-Tools, Qiskit, Combinatorial Optimization, Hybrid Quantum-Classical Algorithms. This software was developed as part of a research internship focused on quantum optimization and serves as a learning and experimentation platform for future research.

•Jul 23, 2026•0 citations

Quantum optimization beyond QUBO for industrial logistics and scheduling

Juan F. R. Hernandez, Pavle Nikacevic, Enrique Solano +4 more

The increasing complexity of industrial scheduling and transport routing problems motivates the study of alternative optimization formulations and computational paradigms. In this work, we study how higher-order unconstrained binary optimization (HUBO) formulations of such problems map onto quantum optimization workflows in both noisy and fault-tolerant regimes. We consider three representative logistics and manufacturing use cases and formulate each as a HUBO problem. This captures process intricacies, such as highly correlated assembly-line scheduling rules, which are difficult to express faithfully with the standard quadratic (QUBO) form, while at the same time reducing the number of binary variables required in the quantum mapping, thus lowering qubit demand. We compare the HUBO formulations with corresponding QUBO encodings, highlighting a key trade-off: while HUBO reduces qubit requirements through compact binary encoding, it introduces higher-order interaction terms that increase circuit depth, limiting feasibility on current quantum hardware. The proposed formulations are validated using classical solvers across several problem instances and benchmark small routing problem instances using bias-field digitized counterdiabatic quantum optimization in classical simulation. We complement these results with a resource and scalability analysis, focusing on the capacitated vehicle routing problem as a representative large-scale industrial use case. Our analysis indicates that while HUBO formulations offer advantages in qubit scaling compared to QUBO encodings, their practical implementation is constrained by gate fidelity, coherence, and circuit depth, making hybrid quantum-classical workflows and early fault-tolerant quantum hardware the most plausible settings for their practical use.

quant-pharXiv:2605.30252•May 29, 2026•0 citations

Quantum Inspired Vehicular Network Optimization for Intelligent Decision Making in Smart Cities

Kamran Ahmad Awan, Sonia Khan, Eman Abdullah Aldakheel +2 more

Connected and automated vehicles require city-scale coordination under strict latency and reliability constraints. However, many existing approaches optimize communication and mobility separately, which can degrade performance during network outages and under compute contention. This paper presents QIVNOM, a quantum-inspired framework that jointly optimizes vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication together with urban traffic control on classical edge--cloud hardware, without requiring a quantum processor. QIVNOM encodes candidate routing--signal plans as probabilistic superpositions and updates them using sphere-projected gradients with annealed sampling to minimize a regularized objective. An entanglement-style regularizer couples networking and mobility decisions, while Tchebycheff multi-objective scalarization with feasibility projection enforces constraints on latency and reliability. The proposed framework is evaluated in METR-LA--calibrated SUMO--OMNeT++/Veins simulations over a $5\times5$~km urban map with IEEE 802.11p and 5G NR sidelink. Results show that QIVNOM reduces mean end-to-end latency to 57.3~ms, approximately $20\%$ lower than the best baseline. Under incident conditions, latency decreases from 79~ms to 62~ms ($-21.5\%$), while under roadside unit (RSU) outages, it decreases from 86~ms to 67~ms ($-22.1\%$). Packet delivery reaches $96.7\%$ (an improvement of $+2.3$ percentage points), and reliability remains $96.7\%$ overall, including $96.8\%$ under RSU outages versus $94.1\%$ for the baseline. In corridor-closure scenarios, travel performance also improves, with average travel time reduced to 12.8~min and congestion lowered to $33\%$, compared with 14.5~min and $37\%$ for the baseline.

cs.NICross-listedarXiv:2603.24971•Mar 27, 2026
Cooperative platooning can reduce the energy use of Connected and Autonomous Vehicle (CAV) fleets, but the routing problem becomes difficult when vehicles must meet on the same road segments at compatible times while moving through unstable urban traffic. This paper develops an edge-assisted, closed-loop evaluation pipeline for platooning-aware vehicle routing. Roadside Units estimate local traffic kinematics from video, classify segment-level flow stability, and activate platooning rewards only on road segments where close-gap coordination is physically appropriate. The resulting multi-vehicle routing problem is written directly as a Quadratic Unconstrained Binary Optimization (QUBO) model, so pairwise platooning interactions are represented as native quadratic Ising terms instead of requiring auxiliary MILP linearization variables. We evaluate the framework using a 24-hour microscopic SUMO simulation of Troy, NY, together with localized IBM Quantum hardware benchmarks. The SUMO study shows an 18.5%18.5\%18.5% reduction in fleet tractive-energy demand relative to a non-cooperative baseline. On 25-active-qubit benchmark instances executed on \texttt{ibm_boston}, Linear-Chain QAOA reduces two-qubit CNOT depth by 66.7%66.7\%66.7% compared with dense QAOA and samples the exact classical ground state with Pfeas=38.6%P_{\text{feas}} = 38.6\%Pfeas​=38.6% and Popt=14.2%P_{\text{opt}} = 14.2\%Popt​=14.2% at p=2p=2p=2. These results suggest that edge perception and shallow quantum optimization can work together as a useful component of closed-loop CAV platoon dispatching.
Abstract: Global logistics networks, intercontinental air-freight consolidators, and maritime energy fleets continuously solve large-scale Mixed-Integer Linear Programs (MILPs) such as the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) and multi-vessel Maritime Inventory Routing Problems (MIRP). When weather disruptions, port congestion, or geopolitical airspace closures invalidate baseline schedules, classical branch-and-cut solvers (Gurobi, CPLEX) experience exponential worst-case tree growth on subproblems exceeding 2,000 binary decision variables, often failing to converge within strict 5-to-15-minute operational re-dispatch windows. Conversely, current Noisy Intermediate-Scale Quantum (NISQ) processors remain limited to 100-to-1,000 physical qubits with finite two-qubit gate fidelity (99.2%−99.8%99.2\%-99.8\%99.2%−99.8%), preventing direct encoding of full enterprise logistics instances. This paper presents a production-grade Quantum-Classical Hybrid Cloud Architecture that bridges enterprise Kubernetes clusters with co-located quantum processing units (QPUs) via AWS Braket Hybrid Jobs and IBM Quantum Qiskit Runtime primitives. Our framework applies Benders and Dantzig-Wolfe column generation on classical cloud HPC nodes to decompose a 25,000-variable global fleet MILP into a continuous master problem and hard combinatorial pricing/routing subproblems of 60 to 140 binary variables. These subproblems are transformed into Quadratic Unconstrained Binary Optimization (QUBO) and Ising Hamiltonians, warm-started from classical linear programming (LP) relaxations (WS-QAOA), compiled with dynamical decoupling and Zero-Noise Extrapolation (ZNE), and executed inside co-located QPU-CPU sessions while a GPU-accelerated Simulated Bifurcation (SB) digital annealer runs in parallel as a deterministic SLA fallback. Evaluated on disruption recovery workloads modeled on ExxonMobil maritime LNG routing and DHL Global Forwarding air-cargo container loading, our hybrid WS-QAOA + Column Generation workflow improves optimality gap closure at a 10-minute SLA cutoff from 6.84% (classical branch-and-bound alone) to 1.42%, reduces emergency re-routing compute wall-clock time by 61.5%, and cuts fleet fuel and charter penalty costs by 4.7%. Research Domain & Specialization Track Master of Science in Information Technology (MSIT) — Quantum Cloud Computing, Hybrid Orchestration, and Operations Research. Core Technical Highlights Empirical Production Case Studies: Rigorous architectural blueprints and telemetry evaluation across enterprise cloud deployments. Four-Tier Reference Architecture: Formal specification from edge ingress to distributed hardware execution pools. Mathematical Formulation: Formally derived algorithmic and optimization models with closed-loop verification. Multi-Modal Benchmarking: Comparative empirical baselines under fault-injection and synthetic workload scaling. Author Information Author: Sonu Kumar SinghRole: Senior Consultant Affiliation: Independent Researcher Publication Status: Preprint / Working Paper (MSIT AI & Cloud Systems Research Series) Reproducibility & Open Science All included architecture diagrams, telemetry schemas, and benchmark curves are open-access under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
The growing complexity and volume of traffic and sensory data in Connected and Autonomous Vehicles (CAVs) make it crucial to develop more effective methods for selecting and extracting features from high-dimensional images and videos. This paper proposes a novel quantum-accelerated feature selection framework utilising the Variational Quantum Eigensolver (VQE) to enhance perception tasks in CAV environments. By decomposing high-dimensional traffic images into compact patches encoded as quantum Hamiltonians, the proposed design aims to support Intelligent Transportation Systems (ITS) tasks such as object detection, vehicle classification, and traffic monitoring in CAVs.The quantum hybrid method serves as a pipeline for extracting selective features that have been optimised using gradient-aware parameter pruning and conditional principal component analysis (PCA). The implementation of this quantum hybrid approach facilitates addressing computational challenges at the edge in embedded vehicular systems, reducing data dimensionality while preserving key discriminative spatial patterns necessary for object detection and traffic monitoring. Although the quantum method currently underperforms in certain clustering and separability metrics, it demonstrates potential as a viable alternative for advanced perception in CAVs.This work lays a foundation for integrating quantum computing into edge-based autonomous vehicle perception, highlighting both opportunities and challenges with near-term quantum hardware.
Introduction Finding the most efficient routes for a fleet of delivery vehicles remains a critical and costly challenge for nearly any logistics business. This core idea is known as the Vehicle Routing Problem (VRP). The primary goal is to minimize operational costs, such as the total travel distance or the number of trucks deployed, while ensuring all the customers are served. Because VRP is NP-hard, finding a perfect solution is often impossible, forcing industries to rely on classical approximate algorithms. However, quantum variational algorithms offer a new paradigm by iteratively optimizing parameters to navigate the solution space, presenting a new and promising avenue for finding better and faster solutions. Methods In this paper, we introduce Symmetric Arc Reduction in Quantum Approximate Optimization Algorithm (SAR-QAOA), which systematically eliminates redundant symmetric routing variables in the binary integer programming formulation prior to the Quadratic Unconstrained Binary Optimization (QUBO) formulation. The proposed SAR-QAOA encodes edge-based VRP while reducing the qubit requirement from n ( n −1) to n ( n −1)/2, thereby improving the resource efficiency of the QUBO formulation by prioritizing regions of the solution space that are more likely to contain high-quality solutions; SAR enhances both search accuracy and computational efficiency. Results Experimental results on noiseless quantum simulators validate the feasibility of the proposed formulation and its integration with the QAOA framework, providing a promising foundation for future investigations on larger routing problems and evolving quantum hardware.
Quantum Optimization Pipeline for the Capacitated Vehicle Routing Problem (CVRP) This repository presents a research-oriented implementation of classical and quantum optimization techniques for solving the Capacitated Vehicle Routing Problem (CVRP). The project serves as an educational and experimental framework for studying hybrid quantum-classical optimization methods and benchmarking their performance on combinatorial optimization problems. The implementation includes: Classical CVRP optimization using Google OR-Tools Mathematical formulation of the routing problem Transformation of the optimization problem into a Quadratic Unconstrained Binary Optimization (QUBO) model Conversion of the QUBO formulation into an Ising Hamiltonian Implementation of the Quantum Approximate Optimization Algorithm (QAOA) using Qiskit Classical versus quantum performance comparison Route visualization and benchmarking Modular Python code designed for experimentation and future extensions The project is intended as a foundation for exploring applications of Quantum Computing, Operations Research, and Hybrid Quantum-Classical Optimization in real-world routing and logistics problems. It provides a reproducible environment for understanding optimization workflows, evaluating quantum algorithms, and extending the framework toward more complex scenarios such as dynamic vehicle routing and emergency ambulance dispatch. Keywords: Quantum Computing, Quantum Optimization, QAOA, QUBO, Ising Model, Capacitated Vehicle Routing Problem (CVRP), Operations Research, OR-Tools, Qiskit, Combinatorial Optimization, Hybrid Quantum-Classical Algorithms. This software was developed as part of a research internship focused on quantum optimization and serves as a learning and experimentation platform for future research.
The increasing complexity of industrial scheduling and transport routing problems motivates the study of alternative optimization formulations and computational paradigms. In this work, we study how higher-order unconstrained binary optimization (HUBO) formulations of such problems map onto quantum optimization workflows in both noisy and fault-tolerant regimes. We consider three representative logistics and manufacturing use cases and formulate each as a HUBO problem. This captures process intricacies, such as highly correlated assembly-line scheduling rules, which are difficult to express faithfully with the standard quadratic (QUBO) form, while at the same time reducing the number of binary variables required in the quantum mapping, thus lowering qubit demand. We compare the HUBO formulations with corresponding QUBO encodings, highlighting a key trade-off: while HUBO reduces qubit requirements through compact binary encoding, it introduces higher-order interaction terms that increase circuit depth, limiting feasibility on current quantum hardware. The proposed formulations are validated using classical solvers across several problem instances and benchmark small routing problem instances using bias-field digitized counterdiabatic quantum optimization in classical simulation. We complement these results with a resource and scalability analysis, focusing on the capacitated vehicle routing problem as a representative large-scale industrial use case. Our analysis indicates that while HUBO formulations offer advantages in qubit scaling compared to QUBO encodings, their practical implementation is constrained by gate fidelity, coherence, and circuit depth, making hybrid quantum-classical workflows and early fault-tolerant quantum hardware the most plausible settings for their practical use.
Connected and automated vehicles require city-scale coordination under strict latency and reliability constraints. However, many existing approaches optimize communication and mobility separately, which can degrade performance during network outages and under compute contention. This paper presents QIVNOM, a quantum-inspired framework that jointly optimizes vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication together with urban traffic control on classical edge--cloud hardware, without requiring a quantum processor. QIVNOM encodes candidate routing--signal plans as probabilistic superpositions and updates them using sphere-projected gradients with annealed sampling to minimize a regularized objective. An entanglement-style regularizer couples networking and mobility decisions, while Tchebycheff multi-objective scalarization with feasibility projection enforces constraints on latency and reliability. The proposed framework is evaluated in METR-LA--calibrated SUMO--OMNeT++/Veins simulations over a 5×55\times55×5~km urban map with IEEE 802.11p and 5G NR sidelink. Results show that QIVNOM reduces mean end-to-end latency to 57.3~ms, approximately 20%20\%20% lower than the best baseline. Under incident conditions, latency decreases from 79~ms to 62~ms (−21.5%-21.5\%−21.5%), while under roadside unit (RSU) outages, it decreases from 86~ms to 67~ms (−22.1%-22.1\%−22.1%). Packet delivery reaches 96.7%96.7\%96.7% (an improvement of +2.3+2.3+2.3 percentage points), and reliability remains 96.7%96.7\%96.7% overall, including 96.8%96.8\%96.8% under RSU outages versus 94.1%94.1\%94.1% for the baseline. In corridor-closure scenarios, travel performance also improves, with average travel time reduced to 12.8~min and congestion lowered to 33%33\%33%, compared with 14.5~min and 37%37\%37% for the baseline.