agent/papers/items/2026-2607-00939-leveraging-llm-based-agentic-systems-to-generate-quantum-applications-for-test-o.md

Paper: Leveraging LLM-Based Agentic Systems to Generate Quantum Applications for Test Optimization

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type: paper title: Leveraging LLM-Based Agentic Systems to Generate Quantum Applications for Test Optimization authors: Ming Tao, Yuechen Li, Tao Yue, Man Zhang, Aitor Arrieta Marcos year: 2026 venue: arXiv url: https://arxiv.org/abs/2607.00939 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-07-01 updated_at: 2026-07-01 status: queued relevance: high topics:

• agent-evaluation
• coding-agent
• multi-agent
• rag
• workflow-agent methods:

benchmarks:

models:

datasets:

• cs.SE
• quant-ph related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 17 collection_queries: multi-agent-llm

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: multi-agent-llm
• inferred topics: agent-evaluation, coding-agent, multi-agent, rag, workflow-agent
• arXiv categories: cs.SE, quant-ph
• collection score: 17

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2607.00939