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.

翻译：随着数据量的持续增长及由此带来的计算需求激增，极端数据科学工作流的兴起已深刻影响着科学计算领域。为应对日益增长的计算需求，量子计算被提出作为解决方案之一。它在分子动力学、量子化学、组合优化及机器学习等众多科学应用中具有显著的理论加速潜力。因此，将量子计算机整合进计算连续体是加速科学计算的一条有前景的路径。然而，科学计算领域仍缺乏必要的工具与专业知识，难以在科学工作流等复杂应用中充分发挥量子计算机的能力。本文首先阐述量子计算的主要特性及其对科学应用的核心优势，进而提出混合量子-经典工作流的形式化定义，探索量子组件的识别方法及其资源映射机制。我们通过实际用例验证相关概念，并定义了一套混合工作流管理系统的软件架构。