Enterprise AI Analysis
Quantum Optimization for Software Engineering: A Survey
This Systematic Literature Review (SLR) analyzes 76 primary studies (2014-2025) on quantum and quantum-inspired optimization for classical Software Engineering (SE). It reveals a growing interdisciplinary field, with most research focusing on SE operations and software testing. While many approaches outperform classical baselines, few show statistically significant improvements. Significant gaps exist in addressing multi-objective problems and certain SE activities like architecture and design. The NISQ era's hardware limitations and challenges in benchmark scalability and parameter tuning remain key research hurdles.
Key Findings at a Glance
0
Primary Studies Analyzed
0
Solutions Outperforming Classical
0
Statistically Significant Improvements
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
2019-2023
Significant Growth Period
Research on quantum and quantum-inspired optimization for SE gained increasing attention starting in 2019, with a peak of 17 papers in 2023.
61
Distinct Publication Venues
The 76 primary studies were published across 61 distinct venues from diverse domains, indicating a highly interdisciplinary field.
| Aspect | Details |
|---|---|
| Journal Publications |
|
| Conference Publications |
|
| Workshop Publications |
|
| Published in SE Venues |
|
56.58%
Focus on SE Operations
The majority of studies (43 papers) target software engineering operations, followed by software testing (21.05%), software quality (13.16%), and software security (10.53%).
| Problem | Number of Studies |
|---|---|
| Test Suite Minimization | 9 |
| Scheduling Problem | 9 |
| Software Failure Prediction | 7 |
| Security Attack Identification | 6 |
| Join Order Problem | 6 |
91.67%
Multi/Many-Objective to Single-Objective Reformulation
A significant portion of multi-objective and many-objective problems were reformulated into single-objective problems, primarily due to inherent algorithmic limitations and hardware constraints.
SLR Methodology Flow
Start
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Define keywords
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Propose inclusion & exclusion criteria
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Keyword searching (Collection Round 1)
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Filtering (Round 1) & Data Discussion
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Paper inclusion & exclusion
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Snowballing (Collection Round 2)
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Forward snowballing & Online Search
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Backward snowballing & Reference Search
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Filtering (Round 2) End
48
Quantum-Inspired Solutions
The majority of solutions are classical (quantum-inspired) and run on existing classical hardware, reflecting current quantum hardware limitations.
| Algorithm Type | Examples | Inspiration |
|---|---|---|
| Quantum-inspired |
|
|
| Pure Quantum |
|
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Hybrid Approaches and Practical Integration
Hybrid solutions, combining quantum algorithms with classical components for problem decomposition or pre/post-processing, are emerging. These reflect the practical need to complement current quantum capabilities, especially given the limitations of the NISQ era. For instance, problem decomposition using D-Wave and classical components is a key strategy for tackling larger problems.
19.74%
Statistically Significant Improvements
Only 15 out of 76 studies (19.74%) demonstrated statistically significant improvements over classical baselines, highlighting a need for more robust empirical evidence.
| Challenge Category | Description |
|---|---|
| Extensibility |
|
| Solution Improvement |
|
| Benchmark Scalability |
|
| Real-world Scenarios |
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| Parameter Tuning |
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Untapped Potential in SE Activities
Seven out of 15 SWEBOK SE activities are not yet covered, including Software Architecture, Software Design, Software Construction, SE Process, SE Models and Methods, SE Professional Practice, and SE Economics. Even within covered areas, many specific tasks and multi-objective problems remain unexplored, indicating significant untapped potential for quantum optimization.
Estimate Your AI-Driven Software Engineering ROI
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Our Proven Implementation Roadmap
A phased approach to integrate quantum-inspired solutions into your existing SE workflows.
Discovery & Assessment
Deep dive into current SE challenges and identify high-impact optimization opportunities.
Solution Design & Customization
Tailor quantum-inspired algorithms to specific problems, reformulating objectives and constraints.
Pilot Program & Validation
Implement solutions on a pilot scale, rigorously evaluating performance against classical baselines.
Scalable Deployment & Integration
Roll out validated solutions across enterprise systems, focusing on seamless integration and ongoing monitoring.
Performance Optimization & Future-Proofing
Continuously refine algorithms, address scalability concerns, and explore new quantum advancements.
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