  With the advent of the Post-Moore era, the scientific community is faced with
the challenge of addressing the demands of current data-intensive machine
learning applications, which are the cornerstone of urgent analytics in
distributed computing. Quantum machine learning could be a solution for the
increasing demand of urgent analytics, providing potential theoretical speedups
and increased space efficiency. However, challenges such as (1) the encoding of
data from the classical to the quantum domain, (2) hyperparameter tuning, and
(3) the integration of quantum hardware into a distributed computing continuum
limit the adoption of quantum machine learning for urgent analytics. In this
work, we investigate the use of Edge computing for the integration of quantum
machine learning into a distributed computing continuum, identifying the main
challenges and possible solutions. Furthermore, exploring the data encoding and
hyperparameter tuning challenges, we present preliminary results for quantum
machine learning analytics on an IoT scenario.
