Isolating Recurring Execution-Dependent Abnormal Patterns on NISQ Quantum Devices

2026-04-19 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Software Development & Engineering, Quantum Computing · Depth: Expert, quick

Summary

A new delta-debugging framework, QRisk, addresses the fundamental gap between compiler-modeled noise and actual hardware execution on NISQ quantum devices. Current quantum compilers approximate noise using calibration-derived costs, but real devices exhibit unexpected, execution-context-dependent errors from specific short gate sequences on qubit neighborhoods. QRisk tackles this by comparing real-device error against calibration-based noise models, transforming hardware-model discrepancies into a size-normalized stochastic fault signal. It then localizes compact gate fragments through segment-level reduction and validates recurring backend-specific patterns across calibration windows. On three IBM Heron r2 backends and 30 qubit layouts, QRisk identified 25 recurring abnormal gate patterns. Eliminating these patterns in compiled circuits reduced excess hardware noise by 24% on ibm_fez (Spearman ρ = 0.515, p = 0.0007) and 45% on ibm_marrakesh (ρ = 0.711, p < 0.0001).

Key takeaway

For quantum software engineers optimizing circuit performance on NISQ devices, understanding and mitigating execution-dependent noise is crucial. You should consider integrating tools like QRisk to identify and eliminate recurring abnormal gate patterns in your compiled circuits. This approach can significantly reduce excess hardware noise, as demonstrated by reductions of 24% on ibm_fez and 45% on ibm_marrakesh, directly improving the fidelity of your quantum computations.

Key insights

Quantum hardware exhibits execution-dependent noise patterns beyond compiler models, which QRisk identifies to improve circuit fidelity.

Principles

• Compiler noise models often miss execution context-dependent errors.
• Stochastic fault signals can be normalized by size for analysis.
• Recurring patterns across calibration windows indicate persistent issues.

Method

QRisk compares real-device error to calibration-based models, converting discrepancies into a size-normalized stochastic fault signal. It then performs segment-level reduction to localize gate fragments and validates recurring patterns across calibration windows.

In practice

• Identify specific gate sequences causing excess noise on quantum hardware.
• Reduce hardware noise by eliminating identified abnormal gate patterns.
• Validate error patterns across different calibration windows.

Topics

• NISQ Devices
• Quantum Noise Models
• Delta Debugging
• Quantum Compilers
• IBM Heron
• Circuit Optimization

Best for: AI Scientist, Research Scientist, AI Hardware Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.