StabilizerBench is a new benchmark for evaluating AI agents on generating, optimizing, and making fault-tolerant stabilizer circuits for quantum error correction, with efficient verification and multi-tier scoring.
Qiskit HumanEval: An evaluation benchmark for quantum code generative models
6 Pith papers cite this work. Polarity classification is still indexing.
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2026 6representative citing papers
QBugLM introduces an agentic benchmarking framework for LLM debugging of quantum software and shows iterative prompting greatly improves repair rates on injected bugs.
A taxonomy-guided RAG system with LLMs reduces hallucinations and improves migration suggestions for Qiskit code compared to unconstrained retrieval.
A layered framework with physical gatekeepers, fidelity analysis against reference VQE circuits, and a consistency metric identifies five LLM failure modes in quantum circuit generation and reveals that some apparent model errors originated in the evaluation harness itself.
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.
Retrieval over a 13,389-example verified PennyLane corpus raises QHack pass@5 from 36/43/24% to 64/68/52% across 2022–2024 with Claude Sonnet 4.6.
citing papers explorer
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StabilizerBench: A Benchmark for AI-Assisted Quantum Error Correction Circuit Synthesis
StabilizerBench is a new benchmark for evaluating AI agents on generating, optimizing, and making fault-tolerant stabilizer circuits for quantum error correction, with efficient verification and multi-tier scoring.
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QBugLM: An Agentic Benchmarking Framework for LLM-based Quantum Software Debugging
QBugLM introduces an agentic benchmarking framework for LLM debugging of quantum software and shows iterative prompting greatly improves repair rates on injected bugs.
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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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Gatekeepers and Hallucinations: A Layered Evaluation Framework for LLM-Driven Quantum Circuit Generation
A layered framework with physical gatekeepers, fidelity analysis against reference VQE circuits, and a consistency metric identifies five LLM failure modes in quantum circuit generation and reveals that some apparent model errors originated in the evaluation harness itself.
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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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PennySynth: RAG-Driven Data Synthesis for Automated Quantum Code Generation
Retrieval over a 13,389-example verified PennyLane corpus raises QHack pass@5 from 36/43/24% to 64/68/52% across 2022–2024 with Claude Sonnet 4.6.