text: 191,32,1509084870999486464,3236251346,Mikel Sanz,"New paper “Quantum Genetic Algorithm with Individuals in Multiple Registers” <LINK> with @raist272 and @Gatgian we propose a subroutine-based fully quantum genetic algorithm distributable among quantum processors @OpenSuperQ @QUANTEK2122 @Ikerbasque @BCAMBilbao <LINK> The use multiple registers allows us to introduce all the natural elements characterizing genetic algorithms: population-based search with selection of many individuals, crossover and mutation. Surprisingly, the mutation subroutine, has small impact on the average performance 2/3 <LINK> Finally, and I like this a lot 🙃, we introduce a quantum channel analysis to prove the exponential convergence of our algorithm and even predict its convergence-ratio. @NquireC @ryc_upvehu @upvehu <LINK>",http://arxiv.org/abs/2203.15039,"Genetic algorithms are heuristic optimization techniques inspired by Darwinian evolution, which are characterized by successfully finding robust solutions for optimization problems. Here, we propose a subroutine-based quantum genetic algorithm with individuals codified in independent registers. This distinctive codification allows our proposal to depict all the fundamental elements characterizing genetic algorithms, i.e. population-based search with selection of many individuals, crossover, and mutation. Our subroutine-based construction permits us to consider several variants of the algorithm. For instance, we firstly analyze the performance of two different quantum cloning machines, a key component of the crossover subroutine. Indeed, we study two paradigmatic examples, namely, the biomimetic cloning of quantum observables and the Bu\v zek-Hillery universal quantum cloning machine, observing a faster average convergence of the former, but better final populations of the latter. Additionally, we analyzed the effect of introducing a mutation subroutine, concluding a minor impact on the average performance. Furthermore, we introduce a quantum channel analysis to prove the exponential convergence of our algorithm and even predict its convergence-ratio. This tool could be extended to formally prove results on the convergence of general non-unitary iteration-based algorithms. ",Quantum Genetic Algorithm with Individuals in Multiple Registers,3,"['New paper “Quantum Genetic Algorithm with Individuals in Multiple Registers” <LINK> with @raist272 and @Gatgian we propose a subroutine-based fully quantum genetic algorithm distributable among quantum processors @OpenSuperQ @QUANTEK2122 @Ikerbasque @BCAMBilbao <LINK>', 'The use multiple registers allows us to introduce all the natural elements characterizing genetic algorithms: population-based search with selection of many individuals, crossover and mutation. Surprisingly, the mutation subroutine, has small impact on the average performance 2/3 https://t.co/l02rWINXWi', 'Finally, and I like this a lot 🙃, we introduce a quantum channel analysis to prove the exponential convergence of our algorithm and even predict its convergence-ratio. @NquireC @ryc_upvehu @upvehu https://t.co/11SXwuDgIt']",22,03,760