AI 中文总结
本文提出一种用于构建混合量子-经典元求解器策略的领域特定语言，围绕该语言构建框架并结合ProvideQ工具箱，以解决混合量子-经典工作流中识别量子候选方案的挑战，所有贡献均开源。
AI 中文摘要
设计混合量子-经典工作流时的一个关键挑战是识别量子候选方案，即确定哪些特定问题可能实现量子优势。在多个候选方案间做选择时，必须考虑问题特有的属性，因为这些属性会从根本上决定量子或经典方法的成功与否，这意味着要成功使用混合量子-经典工作流需要专业知识。为应对这一挑战，本文提出一种领域特定语言（DSL），采用通用表示法来表达解决方案策略的最佳实践，该表示法易于使用和共享。此DSL为设计混合量子-经典工作流提供灵活方法，可针对各类问题自动化决策，支持到问题特定细节的决策，同时保持技术独立性。此外，本文还提出一个围绕该DSL构建的框架，该框架可使用ProvideQ工具箱作为编排层执行已定义的工作流，本文所有贡献均为开源。
英文摘要
A key challenge when designing hybrid quantum-classical workflows is the identification of quantum candidates, that is, determining for which specific problems quantum advantages could potentially be achieved. When choosing between several candidates, it is crucial to consider the characteristics specific to the problem, as these can fundamentally determine how successful quantum or classical approaches will be. This implies that specialized expertise is needed to use hybrid quantum-classical workflows successfully. To address this challenge, we propose a domain-specific language (DSL) to express best-practices in solution strategies using a universal representation that is easy to use and share. This DSL provides a flexible approach to design hybrid quantum-classical workflows and to automate decisions for a wide range of problems, supporting decisions down to problem-specific details while remaining technically independent. Furthermore, we propose a framework that is built around our DSL that enables the execution of defined workflows using the ProvideQ toolbox as an orchestration layer. All contributions from this publication are open source.
Comments13 pages, 3 figures, preprint of a paper that was accepted at the GI Quantum Computing Workshop 2026
