Ganesan, TimothyTimothyGanesanRodríguez Aguilar, RománRománRodríguez AguilarMarmolejo Saucedo, José AntonioJosé AntonioMarmolejo SaucedoVasant, PandianPandianVasant2024-10-212024-10-212023Ganesan, T., Rodriguez-Aguilar, R., Marmolejo-Saucedo, J.A., Vasant, P. (2023). Evolutionary Optimization of Entanglement Distillation Using Chialvo Maps. In: Vasant, P., et al. Intelligent Computing and Optimization. ICO 2023. Lecture Notes in Networks and Systems, vol 852. Springer, Cham. https://doi.org/10.1007/978-3-031-50330-6_297830315032909783031503306https://scripta.up.edu.mx/handle/20.500.12552/1139910.1007/978-3-031-50330-6_2In quantum information theory, entanglement distillation is a key component for designing quantum computer networks and quantum repeaters. In this work, the practical entanglement distillation problem is re-designed in a bilevel optimization framework. The primary goal of this work is to propose and test an effective optimization technique that combines evolutionary algorithms (differential evolution) and the Chialvo map - for solving the bilevel practical entanglement distillation problem. The primary idea is to leverage on the complex dynamical behavior of Chialvo maps to improve the optimization capabilities of the evolutionary algorithm. Analysis on the computational results and comparisons with a standard evolutionary algorithm implementation is presented. ©2023 springer, The authors.enAcceso RestringidoResource Types::text::book::book part