A Physics-Informed Neuro-Fuzzy Framework for Quantum Error Attribution

• URL: http://arxiv.org/abs/2602.21253v1
• Date: Sun, 22 Feb 2026 16:19:51 GMT
• Title: A Physics-Informed Neuro-Fuzzy Framework for Quantum Error Attribution
• Authors: Marwa R. Hassan, Naima Kaabouch,
• Abstract summary: We present a neuro-fuzzy framework that addresses the attribution problem by combining Adaptive Neuro-Fuzzy Inference Systems with physics-grounded feature engineering.<n>We introduce the Bhattacharyya Veto, a hard physical constraint grounded in the Data Processing Inequality.<n>This work establishes a robust, interpretable diagnostic layer that prevents error mitigation techniques from being applied to logically flawed circuits.
• Score: 0.4511923587827302
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 Data Processing Inequality 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.

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