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

TL;DR

This paper introduces a physics-informed neuro-fuzzy framework for quantum error attribution that combines machine learning with physical constraints to improve diagnostic accuracy on large quantum processors.

Contribution

The paper presents a novel neuro-fuzzy system incorporating physical constraints for quantum error attribution, validated on a 156-qubit processor with high accuracy and interpretability.

Findings

Achieves 89.5% effective accuracy in error attribution.

Flags 14.3% of ambiguous cases for manual review.

Identifies fundamental limits like the Z-basis blind spot.

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.