Quantum simulation is a leading candidate for demonstrating practical quantum advantage over classical computation, as it is believed to provide exponentially more compute power than any classical system. It offers new means of studying the behaviour of complex physical systems, for which conventionally software-intensive simulation codes based on numerical high-performance computing are used. Instead, quantum simulations map properties and characteristics of subject systems, for instance chemical molecules, onto quantum devices that then mimic the system under study. Currently, the use of these techniques is largely limited to fundamental science, as the overall approach remains tailored for specific problems: We lack infrastructure and modelling abstractions that are provided by the software engineering community for other computational domains. In this paper, we identify critical gaps in the quantum simulation software stack-particularly the absence of general-purpose frameworks for model specification, Hamiltonian construction, and hardware-aware mappings. We advocate for a modular model-driven engineering (MDE) approach that supports different types of quantum simulation (digital and analogue), and facilitates automation, performance evaluation, and reusability. Through an example from high-energy physics, we outline a vision for a quantum simulation framework capable of supporting scalable, cross-platform simulation workflows.

翻译：量子模拟是展示量子计算相对于经典计算实用优势的主要候选方向，因其被认为能提供超越任何经典系统的指数级计算能力。它提供了研究复杂物理系统行为的新途径，而传统上这类研究依赖于基于数值高性能计算的软件密集型模拟代码。与之相反，量子模拟将目标系统（例如化学分子）的性质与特征映射到量子设备上，由这些设备模拟所研究的系统。目前，这类技术的应用主要局限于基础科学领域，因为整体方法仍针对特定问题定制：我们缺乏软件工程界为其他计算领域提供的基础设施和建模抽象。本文识别了量子模拟软件栈中的关键缺口——特别是缺乏用于模型规范、哈密顿量构建和硬件感知映射的通用框架。我们倡导采用模块化的模型驱动工程方法，以支持不同类型的量子模拟（数字与模拟），并促进自动化、性能评估和可复用性。通过一个高能物理领域的案例，我们勾勒了一个能够支持可扩展、跨平台模拟工作流的量子模拟框架愿景。