Systematic Investigation into Quantum Optimization for Software Engineering
A searchable catalog merging matching studies from the Survey Paper and Analysis Paper datasets.
About the research
From optimization approaches to empirical evidence
Software engineering involves optimization problems such as selecting test cases, configuring software, and allocating resources. These two papers examine how quantum, quantum-inspired, and hybrid approaches are applied to such problems, and how their evaluations are designed and reported.
Survey Paper
Quantum Optimization for Software Engineering: A Survey
This systematic literature review examines quantum and quantum-inspired algorithms for classical software engineering optimization problems. It analyzes 76 primary studies selected from 2,083 publications retrieved across six digital databases. The findings show a concentration on software operations and testing, gaps in other software engineering activities, and relevant research published outside traditional software engineering venues.
Empirical Studies on Quantum Optimization for Software Engineering: A Systematic Analysis
Drawing on primary studies identified by the survey, this systematic analysis examines how quantum, quantum-inspired, and hybrid optimization approaches are evaluated. It covers experimental designs, hyperparameters, case studies, baselines, tooling, and metrics. The findings highlight limited reporting of repetitions and shots, inadequate attention to noise, and a lack of standardized evaluation protocols, especially for quantum-specific metrics. The paper calls for more real-world and open case studies to assess cost-effectiveness and practical utility.
Use the catalog to connect each study's optimization approach with its evaluation evidence. Counts below reflect the current source datasets, which may differ from the study sets reported in the papers.
Study collection
How the primary studies were collected
We collected primary studies through database searching, paper selection, and snowballing. The search queries combined five sets of terms covering quantum computing, optimization problems, optimization techniques, software engineering activities, and software. On January 13, 2025, we searched ACM, IEEE Xplore, Scopus, Springer Nature Link, Web of Science, and Wiley, retrieving 2,083 publications published between 2014 and 2025.
We retained English-language papers that addressed a specific software engineering optimization problem and employed or proposed a quantum-related solution. Theses, surveys, and systematic literature reviews were excluded. All authors participated in selection and confirmed the inclusion decisions, resulting in 39 selected studies.
On April 25, 2025, we used these studies as the starting set for backward and forward snowballing. We examined references and citing papers, applied the same selection criteria, and repeated the process until no further publications were identified. Three authors discussed the candidates until reaching consensus. The original collection comprised 77 primary studies, as retained in the updated survey dataset.
(Note: The catalog contains 77 studies from the original collection. The published Survey Paper reports 76 studies; on October 8, 2026, its repository restored Performance-Driven QUBO for Recommender Systems on Quantum Annealers to updated_selection.csv after its journal publication, bringing the dataset back to 77. The Analysis Paper starts from these 77 studies but excludes one without an experimental evaluation, leaving 76 studies in its analysis. See the survey repository update and the analysis repository explanation.)
Live analysis
What the merged data contains
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Studies by year
Optimization type
Quantum algorithm
Venue
SE activity
Merged catalog
Explore the evidence
Search any field name or value, or select a category or field in Search in to compare papers in a table. Leave the keyword empty to see all reported values in the selected category.
