arXiv AI By Yi Zhou

From Paper to Program: Externalizing and Diagnosing Knowledge Bottlenecks in AI-Assisted Quantum Many-Body Code Generation

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arXiv:2604. 04089v4 Announce Type: replace-cross Abstract: Large language models can write scientific code, but direct paper-to-program translation remains fragile when correctness depends on tacit conventions rather than explicit equations.

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arXiv Machine Learning
Sep 18

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

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.

By Xujun Che, Hanhan Wu, Yuchen Yuan, Chenyang Yu