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Quantum Machine Learning is an interdisciplinary field that combines the principles of quantum physics, quantum computers, and machine learning to enhance computational performance. In recent years, there have been significant advancements in the field of quantum machine learning. Researchers have developed quantum algorithms for various tasks. Furthermore, quantum machine learning has been applied to various domains, such as chemistry, cryptography, and finance. Previous bibliometric surveys in this area covered the period from 2014 to 2020. However, as demonstrated in this paper, the pace of publication has increased significantly from 2018 to the present. Therefore, it has become essential to provide a new analysis of the current state of research in this field. In this paper, we deployed visualization tools to analyze co-authorships, co-occurrences, and keyword density in this research area. Our study covers and analyzes a total of 918 publications from the Web of Science database and 1171 publications from the Scopus database from 2006 to 2022. Following the analysis, we identify research questions, opportunities, and research gaps in the field of Quantum Machine Learning. [ABSTRACT FROM AUTHOR]

Learning Automata (LAs) are adaptive decision-making models designed to find an appropriate action in unknown environments. LAs can be classified into two classes: variable structure and fixed structure. To the best of our knowledge, there is no hybrid model based on both of these classes. In this paper, we propose a model that brings together the benefits of both classes of LAs. In the proposed model, called an HLA, the action switching phase of a fixed structure learning automaton is fused with a variable structure learning automaton. Several computer simulations are conducted to study the performance of the proposed model with respect to the total number of rewards and action switching in addition to the convergence rate. The proposed model is compared to both variable structure and fixed structure learning automata, and in most cases, the numerical results demonstrate its superiority. In order to show the applicability of the HLA, a novel adaptive dropout mechanism in deep neural networks was suggested. The results of the simulations show that the proposed mechanism performs better than the simple dropout mechanism with respect to network accuracy. [ABSTRACT FROM AUTHOR]