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.
Quasar: Quantum assembly code generation using tool-augmented llms via agentic rl.arXiv preprint arXiv:2510.00967, 2025
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Adapts QuantumKatas to Qiskit yielding a 350-task benchmark across 26 categories and evaluates 16 LLMs in 39,200 runs, reporting performance gaps and prompting effects.
QPipe deploys specialized LLM agents for parsing, formulation, code generation, review, execution and verification to produce quantum applications from 20 natural-language test-optimization requirements, reporting 100% compilation and 96.7% execution success with solutions that beat a genetic-algori
citing papers explorer
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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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Qiskit QuantumKatas: Adapting Microsoft's Quantum Computing exercises for LLM evaluation
Adapts QuantumKatas to Qiskit yielding a 350-task benchmark across 26 categories and evaluates 16 LLMs in 39,200 runs, reporting performance gaps and prompting effects.
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Leveraging LLM-Based Agentic Systems to Generate Quantum Applications for Test Optimization
QPipe deploys specialized LLM agents for parsing, formulation, code generation, review, execution and verification to produce quantum applications from 20 natural-language test-optimization requirements, reporting 100% compilation and 96.7% execution success with solutions that beat a genetic-algori