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Krawec, Walter O., Markelon, Sam A..  2018.  Genetic Algorithm to Study Practical Quantum Adversaries. Proceedings of the Genetic and Evolutionary Computation Conference. :1270–1277.

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.

Krawec, Walter O., Nelson, Michael G., Geiss, Eric P..  2017.  Automatic Generation of Optimal Quantum Key Distribution Protocols. Proceedings of the Genetic and Evolutionary Computation Conference. :1153–1160.
Quantum Key Distribution (QKD) allows two parties to establish a shared secret key secure against an all-powerful adversary. Typically, one designs new QKD protocols and then analyzes their maximal tolerated noise mathematically. If the noise in the quantum channel connecting the two parties is higher than this threshold value, they must abort. In this paper we design and evaluate a new real-coded Genetic Algorithm which takes as input statistics on a particular quantum channel (found using standard channel estimation procedures) and outputs a QKD protocol optimized for the specific given channel. We show how this method can be used to find QKD protocols for channels where standard protocols would fail.