This paper is a follow-up of one of the most-cited articles published in the first 20 years of the existence of Physics of Life Reviews. The specific topic is “ant colony optimization”, which is a metaheuristic for solving challenging optimization problems. Due to its inspiration from natural ant colonies' shortest path-finding behavior, this optimization technique forms part of a larger field known as swarm intelligence. After a short introduction to ant colony optimization, we first provide a chronology focusing on algorithmic developments rather than applications. The main part of the paper deals with a bibliometric study of the ant colony optimization literature. Interesting trends concerning, for example, the geographic origin of publications and the change in research focus over time, can be learned from the presented graphs and numbers., The research presented in this paper was supported by grants TED2021-129319B-I00 and PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/501100011033., Peer reviewed

Federated learning (FL) is a recent technique that emerged to handle the vast amount of training data needed in machine learning algorithms while fulfilling data owners’ privacy challenges in such scenarios. Simultaneously, the field of quantum computing (QC), using quantum properties such as entanglement and superposition to perform computation, has experienced exponential growth, theoretically proving to be more efficient in specific machine learning tasks and creating the discipline known as quantum machine learning (QML). Thus, an emerging body of knowledge has started studying the combination of these two research agendas, giving rise to the field of quantum federated learning (QFL). In this review, we systematically classify the existing literature through a novel taxonomy, identify current trends and challenges, and highlight research gaps and future directions to support the continued development of this emerging field., This work was founded partially by the Government of Catalonia Exp. Num. 2023 DI 00071 and by Strategic Platform under the development of an industrial PhD in Quantum Machine Learning. Jesus Cerquides is funded by European Union KHealthInAir, GUARDEN, and Humane-AI projects with No. 101057693, 101060693, and 952026. This work was supported by grant PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/501100011033., Peer reviewed