A Domain-Specific Language for Formulating Hybrid Quantum-Classical Meta-Solver Strategies
2026-08-10 • Software Engineering
Software Engineering
AI summaryⓘ
The authors address the problem of choosing when to use quantum computers over classical ones by creating a special language (DSL) that helps experts describe and share the best ways to solve problems using both types of computers. This language makes it easier to decide which method might work best for specific problems without needing deep technical knowledge each time. They also built a framework that uses this language to run these hybrid workflows automatically, making it practical to apply their ideas. All their work is open source for others to use and build upon.
hybrid quantum-classical workflowsquantum advantagedomain-specific languageProvideQ toolboxorchestration layerproblem-specific optimizationopen source frameworkquantum computingclassical computing
Authors
Nick Poser, Domenik Eichhorn, Ina Schaefer
Abstract
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.