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arXiv Machine Learning By Xujun Che, Hanhan Wu, Yuchen Yuan, Chenyang Yu

QEncodeBench: Can Large Language Models Encode Classical Problems into Verified Quantum Oracles?

Read the original on arXiv Machine Learning →

QEncodeBench evaluates whether large language models can translate classical constraint problems into verified quantum phase oracles. The benchmark measures the correctness of generated circuits using an adversarial self‑validated verifier that checks full solution‑set equivalence while enforcing resource limits. Results show that models lacking a reasoning mode perform poorly, whereas enabling native reasoning improves accuracy tenfold; semantic errors dominate, and neuro‑symbolic pipelines close most gaps by delegating critical composition to deterministic procedures.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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