Decomposition Algorithms for Solving NP-hard Problems on a Quantum Annealer
Abstract
NP-hard problems such as the maximum clique or minimum vertex cover problems, two of Karp’s 21 NP-hard problems, have several applications in computational chemistry, biochemistry and computer network security. Adiabatic quantum annealers can search for the optimum value of such NP-hard optimization problems, given the problem can be embedded on their hardware. However, this is often not possible due to certain limitations of the hardware connectivity structure of the annealer. This paper studies a general framework for a decomposition algorithm for NP-hard graph problems aiming to identify an optimal set of vertices. Our generic algorithm allows us to recursively divide an instance until the generated subproblems can be embedded on the quantum annealer hardware and subsequently solved. Furthermore, the framework is applied to the maximum clique and minimum vertex cover problems, and we propose several pruning and reduction techniques to speed up the recursive decomposition. The performance of both algorithms is assessed in a detailed simulation study.
- Authors:
-
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE Laboratory Directed Research and Development (LDRD) Program
- OSTI Identifier:
- 1822729
- Report Number(s):
- LA-UR-19-30809
Journal ID: ISSN 1939-8018
- Grant/Contract Number:
- 89233218CNA000001
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Signal Processing Systems
- Additional Journal Information:
- Journal Volume: 93; Journal Issue: 4; Journal ID: ISSN 1939-8018
- Publisher:
- Springer
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; Decomposition algorithm; D-Wave; Maximum clique; Minimum vertex cover; NPhard; Optimization.
Citation Formats
Djidjev, Hristo Nikolov, Pelofske, Elijah Autumn Rain, and Hahn, Georg. Decomposition Algorithms for Solving NP-hard Problems on a Quantum Annealer. United States: N. p., 2020.
Web. doi:10.1007/s11265-020-01550-1.
Djidjev, Hristo Nikolov, Pelofske, Elijah Autumn Rain, & Hahn, Georg. Decomposition Algorithms for Solving NP-hard Problems on a Quantum Annealer. United States. https://doi.org/10.1007/s11265-020-01550-1
Djidjev, Hristo Nikolov, Pelofske, Elijah Autumn Rain, and Hahn, Georg. Mon .
"Decomposition Algorithms for Solving NP-hard Problems on a Quantum Annealer". United States. https://doi.org/10.1007/s11265-020-01550-1. https://www.osti.gov/servlets/purl/1822729.
@article{osti_1822729,
title = {Decomposition Algorithms for Solving NP-hard Problems on a Quantum Annealer},
author = {Djidjev, Hristo Nikolov and Pelofske, Elijah Autumn Rain and Hahn, Georg},
abstractNote = {NP-hard problems such as the maximum clique or minimum vertex cover problems, two of Karp’s 21 NP-hard problems, have several applications in computational chemistry, biochemistry and computer network security. Adiabatic quantum annealers can search for the optimum value of such NP-hard optimization problems, given the problem can be embedded on their hardware. However, this is often not possible due to certain limitations of the hardware connectivity structure of the annealer. This paper studies a general framework for a decomposition algorithm for NP-hard graph problems aiming to identify an optimal set of vertices. Our generic algorithm allows us to recursively divide an instance until the generated subproblems can be embedded on the quantum annealer hardware and subsequently solved. Furthermore, the framework is applied to the maximum clique and minimum vertex cover problems, and we propose several pruning and reduction techniques to speed up the recursive decomposition. The performance of both algorithms is assessed in a detailed simulation study.},
doi = {10.1007/s11265-020-01550-1},
journal = {Journal of Signal Processing Systems},
number = 4,
volume = 93,
place = {United States},
year = {Mon Jun 29 00:00:00 EDT 2020},
month = {Mon Jun 29 00:00:00 EDT 2020}
}
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