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QTestBench: A Test Framework for Evaluating LLM-Generated Quantum Code

Paper recorded by Signals 4 on 2026-10-09 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-09 on arXiv · recorded by Signals 4 on 2026-10-12

Category: cs.CL · 自然语言处理 · first seen 2026-10-12

Abstract

Large language models (LLMs) have shown strong results in classical code generation and are increasingly used to write the unit tests that safeguard that code. Whether they can do the same for quantum software is less clear, because quantum programs are probabilistic, depend on numerical tolerances, and are judged by domain-specific checkers, so a well-formed test can still disagree with the bench

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#270 most recent of 660 cs.CL papers we have recorded · ↑ newer: Can Language Models Learn When to Abstain? Policy Learning and Calibra · ↓ older: Tracing and Taming Hallucinated Terms via Subsequence-Association Filt
Cite this page: QTestBench: A Test Framework for Evaluating LLM-Generated Quantum Code: the #270 most recent of 660 cs.CL papers we have recorded (as of 2026-10-09). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/qtestbench-a-test-framework-for-evaluating-llm-generated-quantum-code.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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