  Priority queues are one of the most fundamental and widely used data
structures in computer science. Their primary objective is to efficiently
support the insertion of new elements with assigned priorities and the
extraction of the highest priority element. In this study, we investigate the
design of priority queues within the learning-augmented framework, where
algorithms use potentially inaccurate predictions to enhance their worst-case
performance. We examine three prediction models spanning different use cases,
and show how the predictions can be leveraged to enhance the performance of
priority queue operations. Moreover, we demonstrate the optimality of our
solution and discuss some possible applications.
