VF-QCTRL combines LLMs with physics-informed symbolic reasoning and optimization to produce analytic control protocols that match or exceed conventional solvers across a new 16-task benchmark spanning single/multi-qubit, closed/open, and noisy systems.
Qcircuitnet: A large-scale hierarchical dataset for quantum algorithm design
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Introduces QASM-Eval, the first dataset targeting OpenQASM-3 hardware-facing features for LLM training and evaluation, with an extended verifier for syntax, states, and timelines.
SimpleTES scales test-time evaluation in LLMs to discover state-of-the-art solutions on 21 scientific problems across six domains, outperforming frontier models and optimization pipelines with examples like 2x faster LASSO and new Erdos constructions.
PennyLang dataset of 3,347 PennyLane samples boosts LLM code generation success via RAG from 8.7% to 41.7% for Qwen 7B and 78.8% to 84.8% for LLaMa 4.
citing papers explorer
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Toward General Quantum Control with Physics-Informed Large Language Models
VF-QCTRL combines LLMs with physics-informed symbolic reasoning and optimization to produce analytic control protocols that match or exceed conventional solvers across a new 16-task benchmark spanning single/multi-qubit, closed/open, and noisy systems.
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QASM-Eval: A Dataset to Train and Evaluate LLMs on OpenQASM-3 Beyond Quantum Circuits
Introduces QASM-Eval, the first dataset targeting OpenQASM-3 hardware-facing features for LLM training and evaluation, with an extended verifier for syntax, states, and timelines.
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Evaluation-driven Scaling for Scientific Discovery
SimpleTES scales test-time evaluation in LLMs to discover state-of-the-art solutions on 21 scientific problems across six domains, outperforming frontier models and optimization pipelines with examples like 2x faster LASSO and new Erdos constructions.
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A PennyLane-Centric Dataset to Enhance LLM-based Quantum Code Generation using RAG
PennyLang dataset of 3,347 PennyLane samples boosts LLM code generation success via RAG from 8.7% to 41.7% for Qwen 7B and 78.8% to 84.8% for LLaMa 4.