Isolating Recurring Execution-Dependent Abnormal Patterns on NISQ Quantum Devices

TL;DR

QRisk is a framework that identifies and disrupts recurring, hardware-specific error patterns in NISQ quantum devices, improving circuit fidelity by reducing excess noise.

Contribution

It introduces a method to discover and mitigate backend-specific abnormal error patterns using delta debugging and targeted gate swaps.

Findings

Discovered patterns persist over months on IBM backends.

Disrupting patterns reduces hardware noise by up to 45%.

Patterns are specific to each backend and not predicted by noise models.

Abstract

Quantum compilers rely on calibration-derived noise models to guide circuit mapping and optimization. These models characterize gate and qubit errors independently and miss context-dependent effects such as crosstalk and correlated scheduling errors. As a result, two compiled circuits that score equally under the noise model can behave very differently on real hardware, and the compiler has no mechanism to learn from such recurring mismatches. We present QRisk, a framework that discovers backend-specific abnormal patterns from real hardware executions. QRisk uses delta debugging to isolate compact circuit fragments that consistently produce excess error not predicted by the noise model, then validates their persistence across repeated runs and calibration windows. The verified patterns are stored in a backend-specific pattern database. At compilation time, QRisk scans a compiled circuit for occurrences of known patterns and applies targeted commuting gate swaps to disrupt them, producing a semantically equivalent circuit with fewer abnormal patterns. We evaluate QRisk on two IBM backends (ibm_fez and ibm_marrakesh) using Grover search circuits. On both backends, discovered patterns persist across multiple calibration windows over months. Disrupting these patterns via commuting gate swaps reduces excess hardware noise by 24% on ibm_fez (Spearman $\rho$ = 0.515, p = 0.0007) and 45% on ibm_marrakesh ($\rho$ = 0.711, p < 0.0001), while the noise model predicts identical error for all equivalent circuits. Testing on a third backend confirms that these patterns are backend-specific.