The University of Southampton
University of Southampton Institutional Repository

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

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.

Archive
dataset_eqpo.zip - Dataset
Text

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: http://eprints.soton.ac.uk/id/eprint/417586
ORCID for Dimitrios Alanis: orcid.org/0000-0002-6654-1702
ORCID for Zunaira Babar: orcid.org/0000-0002-7498-4474
ORCID for Hung Viet Nguyen: orcid.org/0000-0001-6349-1044
ORCID for Daryus Chandra: orcid.org/0000-0003-2406-7229
ORCID for Soon Ng: orcid.org/0000-0002-0930-7194
ORCID for Lajos Hanzo: orcid.org/0000-0002-2636-5214

## Catalogue record

Date deposited: 05 Feb 2018 17:31

## Contributors

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