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Research Data: A Quantum-Search-Aided Dynamic Programming Framework for Pareto Optimal Routing in Wireless Multihop Networks

Research Data: A Quantum-Search-Aided Dynamic Programming Framework for Pareto Optimal Routing in Wireless Multihop Networks
Research Data: A Quantum-Search-Aided Dynamic Programming Framework for Pareto Optimal Routing in Wireless Multihop Networks
Dataset for the paper "Quantum Topological Error Correction Codes: The Classical-to-Quantum Isomorphism Perspective".Dimitrios Alanis, Panagiotis Botsinis, Zunaira Babar, Hung Viet Nguyen, Daryus Chandra, Soon Xin Ng, Lajos Hanzo.IEEE Access (accepted).Wireless Multihop Networks (WMHNs) have to strike a trade-off among diverse and often conflicting Quality-of-Service (QoS) requirements. The resultant solutions may be included by the Pareto Front under the concept of Pareto Optimality. However, the problem of finding all the Pareto-optimal routes in WMHNs is classified as NP-hard, since the number of legitimate routes increases exponentially, as the nodes proliferate. Quantum Computing offers an attractive framework of rendering the Pareto-optimal routing problem tractable. In this context, a pair of quantum-assisted algorithms have been proposed, namely the Non-Dominated Quantum Optimization (NDQO) and the Non-Dominated Quantum Iterative Optimization (NDQIO). However, their complexity is proportional to $\sqrt{N}$, where $N$ corresponds to the total number of legitimate routes, thus still failing to find the solutions in "polynomial time". As a remedy, we devise a dynamic programming framework and propose the so-called Evolutionary Quantum Pareto Optimization (EQPO) algorithm. We analytically characterize the complexity imposed by the EQPO algorithm and demonstrate that it succeeds in solving the Pareto-optimal routing problem in polynomial time. Finally, we demonstrate by simulations that the EQPO algorithm achieves a complexity reduction, which is at least an order of magnitude, when compared to its predecessors, albeit at the cost of a modest heuristic accuracy reduction.
Quantum Computing, NDQIO, NDQO, Dynamic Programming, Pareto Optimality, Routing
University of Southampton
Alanis, Dimitrios
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Botsinis, Panagiotis
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Babar, Zunaira
23ede793-1796-449d-b5aa-93a297e5677a
Nguyen, Hung Viet
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Chandra, Daryus
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Ng, Soon
e19a63b0-0f12-4591-ab5f-554820d5f78c
Hanzo, Lajos
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Alanis, Dimitrios
8ae8ead6-3974-4886-8e17-1b4bff1d94e0
Botsinis, Panagiotis
d7927fb0-95ca-4969-9f8c-1c0455524a1f
Babar, Zunaira
23ede793-1796-449d-b5aa-93a297e5677a
Nguyen, Hung Viet
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Chandra, Daryus
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Ng, Soon
e19a63b0-0f12-4591-ab5f-554820d5f78c
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Alanis, Dimitrios, Botsinis, Panagiotis, Babar, Zunaira, Nguyen, Hung Viet, Chandra, Daryus, Ng, Soon and Hanzo, Lajos (2018) Research Data: A Quantum-Search-Aided Dynamic Programming Framework for Pareto Optimal Routing in Wireless Multihop Networks. University of Southampton doi:10.5258/SOTON/D0402 [Dataset]

Record type: Dataset

Abstract

Dataset for the paper "Quantum Topological Error Correction Codes: The Classical-to-Quantum Isomorphism Perspective".Dimitrios Alanis, Panagiotis Botsinis, Zunaira Babar, Hung Viet Nguyen, Daryus Chandra, Soon Xin Ng, Lajos Hanzo.IEEE Access (accepted).Wireless Multihop Networks (WMHNs) have to strike a trade-off among diverse and often conflicting Quality-of-Service (QoS) requirements. The resultant solutions may be included by the Pareto Front under the concept of Pareto Optimality. However, the problem of finding all the Pareto-optimal routes in WMHNs is classified as NP-hard, since the number of legitimate routes increases exponentially, as the nodes proliferate. Quantum Computing offers an attractive framework of rendering the Pareto-optimal routing problem tractable. In this context, a pair of quantum-assisted algorithms have been proposed, namely the Non-Dominated Quantum Optimization (NDQO) and the Non-Dominated Quantum Iterative Optimization (NDQIO). However, their complexity is proportional to $\sqrt{N}$, where $N$ corresponds to the total number of legitimate routes, thus still failing to find the solutions in "polynomial time". As a remedy, we devise a dynamic programming framework and propose the so-called Evolutionary Quantum Pareto Optimization (EQPO) algorithm. We analytically characterize the complexity imposed by the EQPO algorithm and demonstrate that it succeeds in solving the Pareto-optimal routing problem in polynomial time. Finally, we demonstrate by simulations that the EQPO algorithm achieves a complexity reduction, which is at least an order of magnitude, when compared to its predecessors, albeit at the cost of a modest heuristic accuracy reduction.

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More information

Published date: 2018
Keywords: Quantum Computing, NDQIO, NDQO, Dynamic Programming, Pareto Optimality, Routing
Organisations: Southampton Wireless Group, Electronics & Computer Science

Identifiers

Local EPrints ID: 417586
URI: https://eprints.soton.ac.uk/id/eprint/417586
PURE UUID: d6b03ddb-277b-4862-b7d5-659adb359d23
ORCID for Dimitrios Alanis: ORCID iD orcid.org/0000-0002-6654-1702
ORCID for Zunaira Babar: ORCID iD orcid.org/0000-0002-7498-4474
ORCID for Hung Viet Nguyen: ORCID iD orcid.org/0000-0001-6349-1044
ORCID for Daryus Chandra: ORCID iD orcid.org/0000-0003-2406-7229
ORCID for Soon Ng: ORCID iD orcid.org/0000-0002-0930-7194
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

Catalogue record

Date deposited: 05 Feb 2018 17:31
Last modified: 06 Jun 2018 13:15

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Contributors

Creator: Dimitrios Alanis ORCID iD
Creator: Panagiotis Botsinis
Creator: Zunaira Babar ORCID iD
Creator: Hung Viet Nguyen ORCID iD
Creator: Daryus Chandra ORCID iD
Creator: Soon Ng ORCID iD
Creator: Lajos Hanzo ORCID iD

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