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.
Quantum verifiable rewards for post-training qiskit code assistant
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
quant-ph 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Supervised fine-tuning on gate-by-gate quantum simulation traces allows LLMs to achieve near-perfect accuracy in predicting quantum measurement outcomes, with added GRPO improving generalization to larger qubit counts.
citing papers explorer
-
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.
-
Fine-Tuning Large Language Models for Quantum Reasoning
Supervised fine-tuning on gate-by-gate quantum simulation traces allows LLMs to achieve near-perfect accuracy in predicting quantum measurement outcomes, with added GRPO improving generalization to larger qubit counts.