Analysis of the Vehicle Routing Problem Solved via Hybrid Quantum Algorithms in the Presence of Noisy Channels

Mohanty, N; Behera, BK; Ferrie, C

Analysis of the Vehicle Routing Problem Solved via Hybrid Quantum Algorithms in the Presence of Noisy Channels

Mohanty, N Behera, BK Ferrie, C

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Quantum Engineering, 2023, 4, pp. 1-14
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Mohanty, N
dc.contributor.author	Behera, BK
dc.contributor.author	Ferrie, C https://orcid.org/0000-0003-2736-9943
dc.date.accessioned	2024-02-26T01:23:08Z
dc.date.available	2024-02-26T01:23:08Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Transactions on Quantum Engineering, 2023, 4, pp. 1-14
dc.identifier.issn	2689-1808
dc.identifier.issn	2689-1808
dc.identifier.uri	http://hdl.handle.net/10453/175873
dc.description.abstract	The vehicle routing problem (VRP) is an NP-hard optimization problem that has been an interest of research for decades in science and industry. The objective is to plan routes of vehicles to deliver goods to a fixed number of customers with optimal efficiency. Classical tools and methods provide good approximations to reach the optimal global solution. Quantum computing and quantum machine learning provide a new approach to solving combinatorial optimization of problems faster due to inherent speedups of quantum effects. Many solutions of VRP are offered across different quantum computing platforms using hybrid algorithms, such as quantum approximate optimization algorithm and quadratic unconstrained binary optimization. In this work, we build a basic VRP solver for three and four cities using the variational quantum eigensolver on a fixed ansatz. The work is further extended to evaluate the robustness of the solution in several examples of noisy quantum channels. We find that the performance of the quantum algorithm depends heavily on what noise model is used. In general, noise is detrimental, but not equally so among different noise sources.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Quantum Engineering
dc.relation.isbasedon	10.1109/TQE.2023.3303989
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Analysis of the Vehicle Routing Problem Solved via Hybrid Quantum Algorithms in the Presence of Noisy Channels
dc.type	Journal Article
utslib.citation.volume	4
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	open_access	*
dc.date.updated	2024-02-26T01:23:05Z
pubs.publication-status	Published
pubs.volume	4

Abstract:

The vehicle routing problem (VRP) is an NP-hard optimization problem that has been an interest of research for decades in science and industry. The objective is to plan routes of vehicles to deliver goods to a fixed number of customers with optimal efficiency. Classical tools and methods provide good approximations to reach the optimal global solution. Quantum computing and quantum machine learning provide a new approach to solving combinatorial optimization of problems faster due to inherent speedups of quantum effects. Many solutions of VRP are offered across different quantum computing platforms using hybrid algorithms, such as quantum approximate optimization algorithm and quadratic unconstrained binary optimization. In this work, we build a basic VRP solver for three and four cities using the variational quantum eigensolver on a fixed ansatz. The work is further extended to evaluate the robustness of the solution in several examples of noisy quantum channels. We find that the performance of the quantum algorithm depends heavily on what noise model is used. In general, noise is detrimental, but not equally so among different noise sources.

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http://hdl.handle.net/10453/175873