QMP-Bench supplies a realistic test set for AI on quantum many-body problems while PhysVEC uses integrated verifiers to turn unreliable LLM generations into code that passes both syntax and physics checks, outperforming baselines.
https: //arxiv.org/abs/2504.14557 15
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A taxonomy-guided RAG system with LLMs reduces hallucinations and improves migration suggestions for Qiskit code compared to unconstrained retrieval.
Iterative refinement boosts LLM success in generating quantum solvers that match classical results, but more advanced models shift from execution errors to hard-to-detect numerical inaccuracies.
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Towards Verifiable and Self-Correcting AI Physicists for Quantum Many-Body Simulations
QMP-Bench supplies a realistic test set for AI on quantum many-body problems while PhysVEC uses integrated verifiers to turn unreliable LLM generations into code that passes both syntax and physics checks, outperforming baselines.
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Qiskit Code Migration with LLMs
A taxonomy-guided RAG system with LLMs reduces hallucinations and improves migration suggestions for Qiskit code compared to unconstrained retrieval.
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Can LLMs Solve Science or Just Write Code? Evaluating Quantum Solver Generation
Iterative refinement boosts LLM success in generating quantum solvers that match classical results, but more advanced models shift from execution errors to hard-to-detect numerical inaccuracies.