AQUA: an Agile Process to Develop Quantum Annealing Applications

2026-01-20

AI Summary

This work addresses the challenges hindering quantum annealing application development—namely mathematical complexity, hardware constraints, and the absence of systematic software engineering methodologies. To bridge this gap, the authors propose AQUA, the first agile development process specifically tailored for QUBO formulation and quantum annealing. Grounded in design science research, AQUA adapts the Scrum framework into four phases: modeling and evaluation, algorithm selection, agile implementation, and deployment and maintenance, integrating formal modeling with prototype-driven algorithm choice. The feasibility and practicality of AQUA are demonstrated through a real-world credit scoring case study, offering a reusable and systematic engineering framework for developing quantum annealing applications.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Satisfiability

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Quadratic unconstrained binary optimization (QUBO) is a field of operations research that is attracting growing interest due to the recent availability of quantum hardware targeted at solving QUBO problems. However, practical adoption is hindered by mathematical intricacy, hardware constraints, and a lack of sound software engineering processes for QUBO development. This work presents AQUA (Agile QUantum Annealing), an agile lifecycle for QUBO/QA development created through an industry-academia partnership between NetService S.p.A and the University of Cagliari. Using the Design Science Research (DSR) approach, AQUA customizes Scrum to the needs of QUBO/QA development, structuring work into four stages: initial assessment with formal modeling, prototype-driven algorithm selection, agile implementation, and deployment with ongoing maintenance, each gated by milestones. Validated on a real credit-scoring case, AQUA shows feasibility and offers an explicit, systematic QA engineering framework. Key contributions of our work are: a dedicated QUBO/QA software process, its creation and design using DSR approach, and its empirical validation on a simple yet real case study.

Problem

• QUBO
• quantum annealing
• software engineering process
• hardware constraints
• mathematical complexity

Innovation

• QUBO
• Quantum Annealing
• Agile Development
• Design Science Research
• Software Engineering Process