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Genetic algorithm to study practical quantum adversaries

Published: 02 July 2018 Publication History

Abstract

In this paper we show how genetic algorithms can be effectively applied to study the security of arbitrary quantum key distribution (QKD) protocols when faced with adversaries limited to current-day technology. We compare two approaches, both of which take into account practical limitations on the quantum power of an adversary (which can be specified by the user). Our system can be used to determine upper-bounds on noise tolerances of novel QKD protocols in this scenario, thus making it a useful tool for researchers. We compare our algorithm's results with current known numerical results, and also evaluate it on newer, more complex, protocols where no results are currently known.

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Cited By

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  • (2021)Expediting population diversification in evolutionary computation with quantum algorithmInternational Journal of Bio-Inspired Computation10.1504/ijbic.2021.11335617:1(63-73)Online publication date: 1-Jan-2021
  • (2019)Fitness comparison by statistical testing in construction of SAT-based guess-and-determine cryptographic attacksProceedings of the Genetic and Evolutionary Computation Conference10.1145/3321707.3321847(312-320)Online publication date: 13-Jul-2019

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  1. Genetic algorithm to study practical quantum adversaries

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      cover image ACM Conferences
      GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference
      July 2018
      1578 pages
      ISBN:9781450356183
      DOI:10.1145/3205455
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 02 July 2018

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      Author Tags

      1. genetic algorithm
      2. quantum computing
      3. quantum cryptography

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      View all
      • (2021)Expediting population diversification in evolutionary computation with quantum algorithmInternational Journal of Bio-Inspired Computation10.1504/ijbic.2021.11335617:1(63-73)Online publication date: 1-Jan-2021
      • (2019)Fitness comparison by statistical testing in construction of SAT-based guess-and-determine cryptographic attacksProceedings of the Genetic and Evolutionary Computation Conference10.1145/3321707.3321847(312-320)Online publication date: 13-Jul-2019

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