Steiner Traveling Salesman Problem with Quantum Annealing

The Steiner Traveling Salesman Problem (STSP) is a variant of the classical Traveling Salesman Problem. The STSP involves incorporating steiner nodes, which are extra nodes not originally part of the required visit set but that can be added to the route to enhance the overall solution and minimize the total travel cost. Given the NP-hard nature of the STSP, we propose a quantum approach to address it. Specifically, we employ quantum annealing using D-Wave's hardware to explore its potential for solving this problem. To enhance computational feasibility, we develop a preprocessing method that effectively reduces the network size. Our experimental results demonstrate that this reduction technique significantly decreases the problem complexity, making the Quadratic Unconstrained Binary Optimization formulation, the standard input for quantum annealers, better suited for existing quantum hardware. Furthermore, the results highlight the potential of quantum annealing as a promising and innovative approach for solving the STSP.

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Publication Details

Journal
Proceedings of the Genetic and Evolutionary Computation Conference Companion
Published
2025-07-14
DOI
https://doi.org/10.1145/3712255.3734313
Citations
1
Primary Topic
Data Management and Algorithms
Type
article
Field-Weighted Citation Impact
0.46
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article

Steiner Traveling Salesman Problem with Quantum Annealing

Eneko Osaba, Francesca Guerriero, Alessia Ciacco
1 citations
Proceedings of the Genetic and Evolutionary Computation Conference Companion
Data Management and Algorithms
0.46
article

Steiner Traveling Salesman Problem with Quantum Annealing

Eneko Osaba, Francesca Guerriero, Alessia Ciacco
article en
1 citations

Abstract

The Steiner Traveling Salesman Problem (STSP) is a variant of the classical Traveling Salesman Problem. The STSP involves incorporating steiner nodes, which are extra nodes not originally part of the required visit set but that can be added to the route to enhance the overall solution and minimize the total travel cost. Given the NP-hard nature of the STSP, we propose a quantum approach to address it. Specifically, we employ quantum annealing using D-Wave's hardware to explore its potential for solving this problem. To enhance computational feasibility, we develop a preprocessing method that effectively reduces the network size. Our experimental results demonstrate that this reduction technique significantly decreases the problem complexity, making the Quadratic Unconstrained Binary Optimization formulation, the standard input for quantum annealers, better suited for existing quantum hardware. Furthermore, the results highlight the potential of quantum annealing as a promising and innovative approach for solving the STSP.

Proceedings of the Genetic and Evolutionary Computation Conference Companion
Tecnalia (ES), University of Calabria (IT)
Decent work and economic growth
Openalex Percentile: Top 34%
Data Management and Algorithms
0.46
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