Abstract:As quantum processors scale beyond 100 qubits, distinguishing software bugs from stochastic hardware noise becomes a critical diagnostic challenge. We present a neuro-fuzzy framework that addresses this attribution problem by combining Adaptive Neuro-Fuzzy Inference Systems (ANFIS) with physics-grounded feature engineering. We introduce the Bhattacharyya Veto, a hard physical constraint grounded in the empirically characterized weak-noise floor of the device, with CPTP contraction guaranteeing one-sided soundness that prevents the classifier from attributing topologically impossible output distributions to noise. Validated on IBM's 156-qubit Heron r2 processor (ibm_fez) across 105 circuits spanning 17 algorithm families, the framework achieves 89.5% effective accuracy (+/- 5.9% CI). The system implements a safe failure mode, flagging 14.3% of ambiguous cases for manual review rather than forcing low-confidence predictions. We resolve key ambiguities - such as distinguishing correct Grover amplification from bug-induced collapse - and identify fundamental limits of single-basis diagnostics, including a Z-basis blind spot where phase-flip errors remain statistically invisible. This work establishes a robust, interpretable diagnostic layer that prevents error mitigation techniques from being applied to logically flawed circuits.
From: Marwa Hassan [view email]
[v1]
Sun, 22 Feb 2026 16:19:51 UTC (380 KB)
[v2]
Fri, 22 May 2026 22:05:39 UTC (393 KB)
[v3]
Thu, 3 Sep 2026 02:33:50 UTC (399 KB)
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