Liquefied natural gas (LNG) shipping poses unique optimization challenges in global logistics due to strict operational constraints, dynamic environmental conditions, and the need to minimize both costs and delays. This study explores the integration of quantum optimization and digital twin technology to address the complexities of LNG ship routing. Specifically, we formulate the ship routing problem as a mixed-integer linear programming (MILP) problem aimed at minimizing costs and travel time while incorporating constraints such as ship capacities and weather variability. Given the NP-hard nature of the problem, classical optimization methods struggle to scale efficiently. To overcome this, we transform the MILP into a quadratic unconstrained binary optimization (QUBO) format and employ the quantum approximate optimization algorithm (QAOA) to solve it. The proposed framework integrates real-time data via digital twins, enabling dynamic updates to the optimization process.
Liquefied natural gas (LNG) shipping poses unique optimization challenges in global logistics due to strict operational constraints, dynamic environmental conditions, and the need to minimize both costs and delays. This study explores the integration of quantum optimization and digital twin technology to address the complexities of LNG ship routing. Specifically, we formulate the ship routing problem as a mixed-integer linear programming (MILP) problem aimed at minimizing costs and travel time while incorporating constraints such as ship capacities and weather variability. Given the NP-hard nature of the problem, classical optimization methods struggle to scale efficiently. To overcome this, we transform the MILP into a quadratic unconstrained binary optimization (QUBO) format and employ the quantum approximate optimization algorithm (QAOA) to solve it. The proposed framework integrates real-time data via digital twins, enabling dynamic updates to the optimization process.
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.
The logistics industry faces increasing complexities that demand real-time adaptability and efficient decision-making. Traditional computing methods struggle with massive data volumes and intricate optimization problems, but quantum computing offers revolutionary potential. This research explores the transformative possibilities of quantum algorithms, particularly the Quantum Approximate Optimization Algorithm (QAOA), for optimizing logistics operations. We investigate whether quantum computing can revolutionize logistics and supply chain management by addressing inherent complexities. The study delves into the application of quantum algorithms in areas such as route optimization, demand forecasting, and disruption management. Our comprehensive literature review unveils the potential of QAOA in surpassing classical computing for determining efficient shipping routes, considering factors like weather, geopolitical concerns, and disruptions.
The optimization of front-end crude oil scheduling is a critical determinant of refinery profitability and operational stability. However, the coupling of discrete logistics events (e.g., vessel berthing) with continuous material flows (e.g., pipeline transfers) renders this problem an NP-hard Mixed-Integer Linear Programming (MILP) challenge, often intractable for classical solvers at industrial scales. This study proposes a novel hybrid quantum-classical framework to address these computational bottlenecks. We employ Benders Decomposition to decouple the monolithic model into a discrete Master Problem (MP) and a continuous Subproblem (SP). To exploit the search capabilities of quantum computing, the MP is reformulated as a Quadratic Unconstrained Binary Optimization (QUBO) model and solved via a hybrid quantum solver, while the SP enforces mass balance and quality constraints through iterative optimality and feasibility cuts. Extensive experiments on 15 multi-scale instances demonstrate that the proposed framework significantly outperforms traditional metaheuristics (e.g., Genetic Algorithms, Tabu Search), reducing total operating costs by approximately 73--80% and achieving computational speeds comparable to state-of-the-art commercial solvers (Gurobi). By effectively leveraging global optimality cuts, the method overcomes the tendency of heuristic approaches to trap in local optima, providing a robust and scalable solution for complex refinery logistics.
We investigate the potential of the Quantum Approximate Optimization Algorithm (QAOA) for reducing energy consumption in route planning, a key challenge in logistics due to the NP-hard nature of the Traveling Salesman and Vehicle Routing Problems. By encoding route optimization as a Quadratic Unconstrained Binary Optimization (QUBO) problem and implementing QAOA circuits at depth p = 3-5 alongside classical baselines of Simulated Annealing (SA) and Genetic Algorithms (GA), we perform systematic benchmarks on Euclidean graphs of sizes N = 5, 10, and 20. Our results demonstrate that QAOA attains higher solution quality with approximation ratios of 0.953 (N = 5), 0.921 (N = 10), and 0.903 (N = 20), outperforming SA and GA by 2.7-4.4%. Wall-clock runtimes for QAOA are 2-3x faster than SA across all tested sizes, and energy consumption measurements reveal a three-order-of-magnitude reduction, remaining in the picojoule range versus nanojoules for classical methods. Translating these gains to real-world logistics suggests an 8.2% improvement in routing efficiency could save approximately 2.62 EJ of fuel annually in the U.S., avoiding nearly 1.94 x 10^8 tonnes of CO2 emissions. These findings highlight QAOA's promise as a fast, energy-efficient optimizer for sustainable logistics applications and underscore its potential role in next-generation fleet-management systems.
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.
Offshore geological exploration increasingly confronts operational and environmental challenges, including ecological constraints, elevated costs, and limited availability of high-resolution geospatial data. This study proposes a quantum-enhanced simulation framework that integrates Quadratic Unconstrained Binary Optimization (QUBO) with the Quantum Approximate Optimization Algorithm (QAOA) to optimize route planning for early-stage geological reconnaissance in the Guajira Offshore basin, Colombia. The study domain—a 1 km² marine area—is discretized into a 10×10 grid of 100 m × 100 m cells, each assigned a geostructural priority score. The resulting QUBO formulation encodes spatial connectivity, movement rules, and geological relevance into a binary decision structure, which is then solved through hybrid quantum–classical optimization routines. Results indicate that QAOA-based solutions produce coherent and efficient exploration routes that prioritize high-value geological targets while respecting spatial continuity and energy limitations. The proposed framework offers a promising pathway toward low-impact, cost-effective offshore exploration strategies and is scalable to broader maritime contexts. Additionally, it is compatible with future extensions involving AI-driven geological classification and real-time, adaptive route planning using multi-agent systems.