As a review summarizing what has been done (up to 2017) already exists, it is advisable to start from there. The paper written by Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe and Seth Lloyd, focuses on quantum basic linear algebra subroutines (BLAS) -- as Fourier transforms, finding eigenvectors and eigenvalues, etc -- which are heavily used in machine learning algorithms, highlighting the advantages of using quantum rather than classical hardware. Indeed, the achievements are mainly in computational speed, although discussions on quantum support vector machine and quantum kernel appears. There is a very good introduction to quantum annealing and quantum Boltzman machine. An updated version (2018) is available on arXiv, although you cannot expect an over-comprehensive summary as the field of quantum computing is having a revolution each week. Nevertheless, the authors are among the pioneers thinking about the combination of both fields.

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Language acquisition in young children is apparently connected with their ability to detect patterns. In their learning process, they search for patterns in the data set that help them identify and optimize grammar structures in order to properly acquire the language. Likewise, online translators use algorithms through machine learning techniques to optimize their translation engines to produce well-rounded and understandable outcomes. Even though many translations did not make much sense at all at the beginning, in these past years we have been able to see major improvements thanks to machine learning. Machine learning techniques use mathematical algorithms and tools to search for patterns in data.