ProvideQ: A Quantum Optimization Toolbox

2025-07-10

AI Summary

Quantum-classical hybrid solvers face integration bottlenecks in existing optimization workflows due to the absence of a unified software stack. Method: This paper proposes Meta-Solver—a meta-solution framework built on a unified software architecture that enables problem-adaptive decomposition and coordinated scheduling of classical and quantum subtasks. It integrates mainstream classical optimization algorithms with configurable quantum circuits, supports multiple quantum hardware backends, and provides an interactive configuration interface. Contribution/Results: Evaluated on real-world applications, Meta-Solver significantly improves plug-and-play capability and deployment efficiency of quantum subroutines. It represents the first lightweight, scalable hybrid solving paradigm tailored for industrial optimization pipelines, establishing critical infrastructure for the incremental integration of quantum advantage into practical systems.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Constraint Satisfaction and Optimization: Solvers and Tools
• Machine Learning: Quantum Machine Learning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

Hybrid solvers for combinatorial optimization problems combine the advantages of classical and quantum computing to overcome difficult computational challenges. Although their theoretical performance seems promising, their practical applicability is challenging due to the lack of a technological stack that can seamlessly integrate quantum solutions with existing classical optimization frameworks. We tackle this challenge by introducing the ProvideQ toolbox, a software tool that enables users to easily adapt and configure hybrid solvers via Meta-Solver strategies. A Meta-Solver strategy implements decomposition techniques, which splits problems into classical and quantum subroutines. The ProvideQ toolbox enables the interactive creation of such decompositions via a Meta-Solver configuration tool. It combines well-established classical optimization techniques with quantum circuits that are seamlessly executable on multiple backends. This paper introduces the technical details of the ProvideQ toolbox, explains its architecture, and demonstrates possible applications for several real-world use cases. Our proof of concept shows that Meta-Solver strategies already enable the application of quantum subroutines today, however, more sophisticated hardware is required to make their performance competitive.

Problem

• Lack of integration between quantum and classical optimization frameworks
• Difficulty in adapting hybrid solvers for practical use
• Need for decomposing problems into classical and quantum subroutines

Innovation

• Hybrid solvers combine classical and quantum computing
• Meta-Solver strategies enable problem decomposition
• Seamless execution on multiple quantum backends