physics-informed-quantum-error-attribution

name	physics-informed-quantum-error-attribution
category	ai_collection
description	Neuro-fuzzy framework for quantum error attribution using physics-informed machine learning. Combines ANFIS with physics-grounded features (Bhattacharyya Veto, Data Processing Inequality) to distinguish software bugs from hardware noise in quantum processors. Validated on 156-qubit IBM Heron r2.
tags	["quantum-computing","systems-engineering","error-attribution","neuro-fuzzy","diagnostic"]
arxiv_id	2602.21253
arxiv_url	https://arxiv.org/abs/2602.21253
trigger_words	["quantum error attribution","quantum debugging","quantum diagnostics","neuro-fuzzy","ANFIS","quantum noise","Bhattacharyya Veto","quantum error mitigation","quantum software testing"]

Physics-Informed Quantum Error Attribution

Overview

As quantum processors scale beyond 100 qubits, distinguishing software bugs from stochastic hardware noise becomes a critical diagnostic challenge. This methodology provides a robust, interpretable diagnostic layer that prevents error mitigation techniques from being applied to logically flawed circuits.

Core Methodology

1. Adaptive Neuro-Fuzzy Inference System (ANFIS)

• Combine fuzzy logic interpretability with neural network learning capacity
• Input features: circuit depth, gate counts, topology, expected output distributions
• Output: classification of error source (software bug vs. hardware noise vs. ambiguous)
• Achieves 89.5% effective accuracy (+/- 5.9% CI)

2. Bhattacharyya Veto (Hard Physical Constraint)

• Grounded in the Data Processing Inequality (DPI)
• Prevents the classifier from attributing topologically impossible output distributions to noise
• If output distribution violates DPI constraints, automatically flag as software bug
• Mathematically: D(p||q) >= D(E(p)||E(q)) for any quantum channel E
• Violations indicate the error cannot be explained by physical noise processes

3. Safe Failure Mode

• Flag ambiguous cases (14.3% of circuits) for manual review
• Never force low-confidence predictions
• Confidence thresholding based on fuzzy membership functions

4. Diagnostic Limitations

• Z-basis blind spot: Phase-flip errors remain statistically invisible in Z-basis measurement
• Single-basis diagnostics are fundamentally insufficient
• Recommend multi-basis measurement for comprehensive diagnostics

Implementation Steps

1. Feature Engineering

   • Extract circuit topology features
   • Compute expected vs. observed output distributions
   • Calculate Bhattacharyya distances between distributions

2. ANFIS Training

   • Train on labeled examples of known bugs vs. noise
   • Use fuzzy rule generation from physical constraints
   • Validate with cross-validation across algorithm families

3. Attribution Pipeline

   • Run circuit on quantum hardware
   • Compare output to classical simulation
   • Apply Bhattacharyya Veto check first
   • If passes veto, run ANFIS classifier
   • Return: bug / noise / ambiguous

4. Error Mitigation Integration

   • Only apply error mitigation if attribution = noise
   • Skip mitigation if attribution = bug (fix circuit first)
   • Request manual review if attribution = ambiguous

Validation

• Validated on IBM 156-qubit Heron r2 processor (ibm_fez)
• 105 circuits across 17 algorithm families
• Resolves ambiguities: distinguishing correct Grover amplification from bug-induced collapse

Key Insights

1. Physics-informed constraints dramatically improve diagnostic accuracy
2. Safe failure modes are essential for production quantum debugging
3. Single-basis measurement is fundamentally insufficient
4. Error attribution must precede error mitigation

Application

Use when:

• Debugging quantum circuits on NISQ hardware
• Distinguishing algorithmic bugs from hardware noise
• Designing quantum software testing pipelines
• Building quantum diagnostic tools