Leveraging LLM-Based Agentic Systems to Generate Quantum Applications for Test Optimization
LLM-based agentic systems like QPipe can autonomously generate quantum applications from natural-language requirements, potentially revolutionizing software engineering optimization.
- QPipe is a multi-agent architecture that translates NL requirements into traceable quantum-application workflows
- Evaluates on 20 NL requirements with real-world benchmarks and test-optimization problems
- Achieves 100% code compilation success rate and 96.7% application execution success rate
Full summary
The paper introduces QPipe, a large language model (LLM)-based multi-agent system designed to autonomously generate quantum applications from natural-language requirements for test optimization tasks. Evaluated on 20 real-world benchmarks, QPipe demonstrates high success rates in code compilation and application execution, with average generation costs of 260.1 seconds and 1.89M tokens per requirement. The generated applications outperform an offline genetic algorithm baseline in most cases, highlighting the potential of agentic coordination for quantum software engineering.