  The increasing growth of data volume, and the consequent explosion in demand
for computational power, are affecting scientific computing, as shown by the
rise of extreme data scientific workflows. As the need for computing power
increases, quantum computing has been proposed as a way to deliver it. It may
provide significant theoretical speedups for many scientific applications
(i.e., molecular dynamics, quantum chemistry, combinatorial optimization, and
machine learning). Therefore, integrating quantum computers into the computing
continuum constitutes a promising way to speed up scientific computation.
However, the scientific computing community still lacks the necessary tools and
expertise to fully harness the power of quantum computers in the execution of
complex applications such as scientific workflows. In this work, we describe
the main characteristics of quantum computing and its main benefits for
scientific applications, then we formalize hybrid quantum-classic workflows,
explore how to identify quantum components and map them onto resources. We
demonstrate concepts on a real use case and define a software architecture for
a hybrid workflow management system.
