New Hybrid Quantum Annealing Algorithms for Solving Vehicle Routing Problem

Borowski, Michał; Góra, Paweł; Karnas, Katarzyna; Błajda, Mateusz; Król, K.; Matyjasek, Artur; Burczyk, Damian; Szewczyk, Miron; Kutwin, Michał

doi:10.1007/978-3-030-50433-5_42

Cited by 34 publications

(29 citation statements)

References 19 publications

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“…The CVRP has clear real world significance because improving vehicle schedules, even by a tiny proportion, can lead to a large reduction in the transportation costs over time. Such problems have very recently been the subject of quantum algorithm development, in particular, using the quantum annealing approach [7][8][9][10]. This paper, however, focuses on the application of QWOA to the Capacitated Vehicle Routing Problem (CVRP), an algorithm that operates within the gatebased model of quantum computation.…”

Section: Introductionmentioning

confidence: 99%

Quantum Walk-Based Vehicle Routing Optimisation

et al. 2021

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This paper demonstrates the applicability of the Quantum Walk-based Optimisation Algorithm (QWOA) to the Capacitated Vehicle Routing Problem (CVRP). Efficient algorithms are developed for the indexing and unindexing of the solution space and for implementing the required alternating phase-walk unitaries, which are the core components of QWOA. Results of numerical simulation demonstrate that the QWOA is capable of producing convergence to near-optimal solutions for a randomly generated eight location CVRP. Preparation of the amplified quantum state in this example problem is demonstrated to produce higher-quality solutions than expected from classical random sampling of equivalent computational effort.

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Section: Introductionmentioning

confidence: 99%

Quantum Walk-Based Vehicle Routing Optimisation

et al. 2021

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“…There has also been some ongoing research on using quantum algorithms to solve the TSP problem, and we can mention the various attempts of QAOA [31][32][33] and QA [34,35]. Some other related work includes [36,37], which use QA to solve vehicle routing problem, a generalization of TSP to multiple vehicles.…”

Section: Tsp Problem and Its Variantsmentioning

confidence: 99%

Unconstrained binary models of the travelling salesman problem variants for quantum optimization

Salehi

Glos

Miszczak

2022

Quantum Inf Process

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Quantum computing is offering a novel perspective for solving combinatorial optimization problems. To fully explore the possibilities offered by quantum computers, the problems need to be formulated as unconstrained binary models, taking into account limitation and advantages of quantum devices. In this work, we provide a detailed analysis of the travelling salesman problem with time windows (TSPTW) in the context of solving it on a quantum computer. We introduce quadratic unconstrained binary optimization and higher-order binary optimization formulations of this problem. We demonstrate the advantages of edge-based and node-based formulations of the TSPTW problem. Additionally, we investigate the experimental realization of the presented methods on a quantum annealing device. The provided results pave the path for utilizing quantum computer for a variety of real-world tasks which can be cast in the form of travelling salesman problem with time windows.

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“…Such quantum computing may be applied as the algorithm's core applicable to solve the railway dispatching problem on the local scale. Our paper follows the research efforts towards solving transportation-related problems using quantum annealers [21,22,23] or quantum approximated optimization algorithm (QAOA) [24].…”

Section: Introductionmentioning

confidence: 99%

Quadratic and Higher-Order Unconstrained Binary Optimization of Railway Rescheduling for Quantum Computing

Domino¹,

Kundu²,

Salehi³

et al. 2021

Preprint

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The consequences of disruptions in railway traffic are the primary cause of passengers' dissatisfaction. Hence, appropriate dispatching decisions are necessary (e.g., by assigning the order of trains), given the numerous restrictions of traffic nature. The latter is perceived as an NPhard problem. This paper outlines QUBO (quadratic unconstrained binary optimization) and HOBO (higher-order binary optimization) representations for dispatching problems of railway traffic management. Specifically, we consider minimal span between trains, minimal stay on stations, station/track occupation, and rolling stock circulation. The main result is the hybrid algorithm to deal with disturbances in rail traffic on single-, double-and multi-track lines; the demonstrative model illustrates the issue briefly. This algorithm can solve railway dispatching problems using the quantum annealer or any other QUBO-based optimization device.

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New Hybrid Quantum Annealing Algorithms for Solving Vehicle Routing Problem

Cited by 34 publications

References 19 publications

Quantum Walk-Based Vehicle Routing Optimisation

Quantum Walk-Based Vehicle Routing Optimisation

Unconstrained binary models of the travelling salesman problem variants for quantum optimization

Quadratic and Higher-Order Unconstrained Binary Optimization of Railway Rescheduling for Quantum Computing

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