An ANFIS classifier with a Bhattacharyya-distance veto reports 89.5% effective accuracy separating quantum hardware noise from software bugs, but its main features presuppose the ground-truth circuit and its veto threshold is fitted to the evaluation data.
Observation of entanglement transition of pseudo-random mixed states
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A Physics-Informed Neuro-Fuzzy Framework for Quantum Error Attribution
An ANFIS classifier with a Bhattacharyya-distance veto reports 89.5% effective accuracy separating quantum hardware noise from software bugs, but its main features presuppose the ground-truth circuit and its veto threshold is fitted to the evaluation data.