HIGHLIGHTS
SUMMARY
In this direction, various types of quantum machine_learning models have been introduced and studied extensively. The results provide a more comprehensive view of quantum machine_learning models as well as insights on the compatibility of different models with NISQ constraints. Next to variational quantum eigensolvers in chemistry4 and variants of the quantum approximate optimization algorithm5, machine_learning approaches based on such parametrized quantum circuits6 stand as some of the most promising practical applications to yield quantum advantages. In essence, a supervised machine_learning problem often reduces to the task of fitting a parametrized function-also referred to . . .
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