arxiv: 2604.17519 · v1 · submitted 2026-04-19 · 💻 cs.SE

Recognition: unknown

Isolating Recurring Execution-Dependent Abnormal Patterns on NISQ Quantum Devices

Haotang Li, Jiyuan Wang, Qian Zhang, Sen He, Zhenyu Qi

Authors on Pith no claims yet

Pith reviewed 2026-05-10 05:20 UTC · model grok-4.3

classification 💻 cs.SE

keywords NISQquantum computingnoise modelsdelta debuggingcircuit optimizationerror mitigationcrosstalkIBM quantum

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The pith

QRisk isolates recurring circuit fragments on NISQ hardware that cause excess errors beyond noise model predictions and mitigates them with commuting gate swaps.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Standard quantum noise models characterize gates and qubits independently and therefore cannot capture context-dependent effects such as crosstalk or correlated scheduling errors. Two circuits that receive identical error scores under these models can nevertheless produce markedly different results on real devices. QRisk applies delta debugging to execution traces to extract compact, recurring fragments responsible for the unexplained excess error, validates their stability across calibration cycles, and stores them in a backend-specific database. During compilation the framework scans new circuits for matches and replaces them with semantically equivalent versions that use commuting gate swaps to break the patterns. On Grover search circuits this yields measured reductions in excess noise of 24 percent on one IBM backend and 45 percent on another while the noise model continues to assign the same cost to both versions.

Core claim

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. On Grover search circuits the method reduces excess hardware noise by 24 percent on ibm_fez and 45 percent on ibm_marrakesh while the noise model predicts identical error for all equivalent电路.

What carries the argument

Backend-specific pattern database populated by delta-debugging isolation of execution-dependent fragments, combined with targeted commuting-gate swaps that eliminate those fragments without altering circuit semantics.

If this is right

Compilers can improve real-device performance by learning persistent mismatches between modeled and observed error rather than relying only on calibration data.
The identified patterns remain stable across multiple calibration windows spanning months, enabling long-term mitigation without repeated discovery.
The mitigation is device-specific, as patterns discovered on one backend do not appear on a third tested device.
Two circuits that are indistinguishable under the noise model can be reliably distinguished by their actual error rates once abnormal patterns are removed.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Each new backend will require its own pattern-discovery campaign because the abnormal fragments are hardware-specific.
The technique offers a practical way to incorporate runtime feedback into compilation without requiring changes to the underlying noise model.
Similar isolation methods could be applied to other quantum algorithms to determine whether the patterns are algorithm-dependent or general hardware traits.
The correlation between pattern count and excess noise suggests that pattern avoidance could become a standard post-mapping optimization step.

Load-bearing premise

The isolated fragments are causally responsible for the observed excess error rather than merely correlated with other unmodeled hardware effects.

What would settle it

Applying the commuting gate swaps to the discovered patterns and measuring no statistically significant drop in excess noise on the same hardware runs, or finding that the noise model assigns different costs to the swapped circuits.

Figures

Figures reproduced from arXiv: 2604.17519 by Haotang Li, Jiyuan Wang, Qian Zhang, Sen He, Zhenyu Qi.

**Figure 1.** Figure 1: Motivating example on ibm_marrakesh (qubits 6, 7, 8, 17). A compiled Grover circuit contains a 4-gate subsequence that causes abnormally high hardware error (Identify). A single commuting gate swap disperses the pattern without changing the circuit’s semantics (Swap). The transformed circuit reduces TVD from 0.058 to 0.032, a 44.5% error reduction, even though the noise model predicts identical error for b… view at source ↗

**Figure 2.** Figure 2: QRisk framework overview. The offline stage discovers abnormal patterns through delta debugging with a ratio-based oracle, then verifies their persistence across repeated runs. The online stage transforms a compiled circuit by applying targeted commuting gate swaps to disrupt discovered patterns. Segment 1 Segment 2 q0 𝑆𝑋 𝑅𝑍 q1 𝑅𝑍 𝑆𝑋 q2 𝑅𝑍 𝑆𝑋 𝑅𝑍 𝑅𝑍 m1 m2 m3 m4 m5 m6 [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Figure 3: A 3-qubit circuit partitioned into 6 moments and 2 segments. Each moment groups gates that can execute in parallel (e.g., 𝑚1 contains SX on 𝑞0 and RZ on 𝑞2). Consecutive moments are grouped into fixed-size segments (here 3 moments each), which serve as the atomic units for delta debugging. a small example circuit. For the running example, the 151- operation Grover circuit contains 107 moments (parallelism… view at source ↗

**Figure 4.** Figure 4: Commuting gate swap on the ibm_fez pattern. Swapping rz(107,0.39) past cz(108,107) breaks the contiguous 4-gate subsequence while preserving circuit semantics. The original 4-gate subsequence no longer appears as a contiguous block. The circuit is semantically equivalent but free of the abnormal pattern. 4 Evaluation In this section, we evaluate QRisk empirically. We describe the experimental setup (Sec… view at source ↗

**Figure 5.** Figure 5: TVD(noisy,real) vs. surviving pattern count (10 circuits per group). (a) and (b) show monotonically increasing excess hardware noise on two backends. (c) runs the same ibm_fez circuits on a different chip, showing no effect [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

QRisk isolates hardware-specific error patterns via delta debugging and cuts excess noise with targeted gate swaps on IBM devices, but the gains may stem from reordering rather than the patterns themselves.

read the letter

The core contribution is a practical loop that runs circuits on real NISQ hardware, uses delta debugging to pull out small recurring fragments that produce more error than the calibration model predicts, stores those patterns per backend, and then rewrites new circuits to break the fragments with commuting swaps. The swaps keep the circuit logically identical, so the standard noise model sees no difference, yet the hardware runs show lower excess error. They demonstrate this on Grover search circuits across two IBM backends, with the patterns holding up over multiple calibration windows spanning months, and report 24% and 45% reductions in excess noise with clear Spearman correlations and p-values. The backend-specific nature is also shown on a third device. That is real, direct hardware evidence rather than simulation or fitting to a model. The approach is straightforward to understand and targets a gap that static compilers actually hit in practice. The main soft spot is the missing control for causality. Any commuting swap changes gate ordering, scheduling, and qubit interactions, which can affect crosstalk or timing noise on their own. Without an ablation that applies a similar number of non-pattern-specific swaps to the same circuits and measures the difference, the reported gains could be a general side-effect of reordering instead of the targeted disruption of the delta-debugged fragments. The abstract also leaves some details on exact statistical matching of circuit depth and other factors implicit. This paper is aimed at researchers building or tuning quantum compilers for current hardware. A reader working on noise-aware mapping or runtime mitigation would get concrete ideas and data to build on. It is worth sending to peer review because the empirical grounding on real devices is solid enough to merit referee scrutiny, even if the controls need tightening in revision.

Referee Report

2 major / 2 minor

Summary. The paper introduces QRisk, a framework that applies delta debugging to hardware executions on NISQ devices to isolate compact, recurring circuit fragments producing excess error beyond predictions from standard calibration-based noise models. These patterns are validated for persistence across multiple calibration windows spanning months on IBM backends; at compile time, QRisk detects them in Grover search circuits and applies targeted commuting gate swaps to produce semantically equivalent circuits with fewer such patterns. Empirical results show 24% and 45% reductions in excess hardware noise on ibm_fez and ibm_marrakesh respectively, with reported Spearman correlations and p-values, while the noise model assigns identical error to all equivalent circuits; patterns are shown to be backend-specific.

Significance. If the causal link between pattern disruption and noise reduction holds, the work provides a pragmatic, backend-specific method to mitigate context-dependent noise (crosstalk, scheduling effects) that standard models miss, improving compiled circuit fidelity without semantic changes. Strengths include direct hardware measurements rather than model fitting, explicit persistence validation over time, and statistical reporting; these could inform future quantum compilers if reproducibility and controls are strengthened.

major comments (2)

[Evaluation of pattern disruption (ibm_fez and ibm_marrakesh results)] The central claim attributes the reported 24% (ibm_fez) and 45% (ibm_marrakesh) excess-noise reductions specifically to disruption of the delta-debugged patterns. However, the evaluation provides no ablation in which an equivalent number of commuting swaps that do not target the identified patterns are applied to the same circuits. Any reordering alters gate scheduling, qubit interactions, and timing, which can affect unmodeled hardware effects independently of the patterns; without this control, the reduction cannot be isolated from general reordering side-effects.
[QRisk framework and pattern discovery] The description of the delta-debugging procedure used to isolate abnormal fragments lacks concrete implementation details required to assess reproducibility and soundness. No information is given on fragment granularity, the precise statistical threshold for declaring 'excess error' relative to the noise model, or how circuit depth and other structural factors were controlled when comparing pattern-containing vs. pattern-free executions.

minor comments (2)

[Abstract and §1] The abstract and introduction refer to 'delta debugging' without a brief inline definition or reference tailored to the quantum-circuit setting, which may hinder readers outside software-engineering debugging literature.
[Statistical reporting in evaluation] The reported Spearman ρ and p-values are useful, but the text does not state whether multiple-comparison corrections were applied across the two backends and multiple calibration windows.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below, acknowledging where additional controls or details would strengthen the work, and describe the revisions we will incorporate.

read point-by-point responses

Referee: [Evaluation of pattern disruption (ibm_fez and ibm_marrakesh results)] The central claim attributes the reported 24% (ibm_fez) and 45% (ibm_marrakesh) excess-noise reductions specifically to disruption of the delta-debugged patterns. However, the evaluation provides no ablation in which an equivalent number of commuting swaps that do not target the identified patterns are applied to the same circuits. Any reordering alters gate scheduling, qubit interactions, and timing, which can affect unmodeled hardware effects independently of the patterns; without this control, the reduction cannot be isolated from general reordering side-effects.

Authors: We agree that the absence of a non-targeted swap control leaves open the possibility that general reordering effects contribute to the observed reductions. Our current evaluation selects only swaps that disrupt the delta-debugged patterns while preserving semantics and reports Spearman correlations between the count of disrupted patterns and excess-noise reduction (with the noise model assigning identical scores to all variants). We will add a dedicated limitations subsection discussing potential confounding from scheduling and timing changes, and we will attempt to include results from a small set of random commuting-swap controls on the same Grover instances if additional hardware time is granted. This addresses the concern without altering the core claims. revision: partial
Referee: [QRisk framework and pattern discovery] The description of the delta-debugging procedure used to isolate abnormal fragments lacks concrete implementation details required to assess reproducibility and soundness. No information is given on fragment granularity, the precise statistical threshold for declaring 'excess error' relative to the noise model, or how circuit depth and other structural factors were controlled when comparing pattern-containing vs. pattern-free executions.

Authors: We acknowledge that the methods section is currently high-level. The delta-debugging procedure operates on gate-level fragments of size 3–5 gates, declares a fragment abnormal when its measured error exceeds the noise-model prediction by more than two standard deviations (computed from 10,000 shots per circuit), and controls for depth by generating matched-depth variants that differ only in the presence/absence of the candidate pattern. We will expand the methods section with pseudocode, exact threshold formulas, and the depth-matching procedure in the revised manuscript. revision: yes

Circularity Check

0 steps flagged

No significant circularity; results rest on direct hardware measurements

full rationale

The paper's derivation chain relies on empirical isolation of patterns via delta debugging on real IBM hardware executions, followed by persistence validation across calibration windows and direct measurement of noise reduction after commuting swaps. The noise model is invoked only as a baseline that assigns identical scores to equivalent circuits; the reported 24-45% reductions and Spearman correlations are computed from hardware data, not from any fitted parameter or self-referential prediction. No self-citations, ansatzes, or uniqueness theorems are load-bearing, and no step renames a known result or equates a prediction to its input by construction. The central claims remain externally falsifiable through additional hardware runs.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The work rests on empirical observation rather than new theoretical axioms; it assumes standard quantum gate commutation rules and that delta debugging can isolate causal error sources from execution traces.

axioms (2)

standard math Commuting gates can be swapped without changing circuit semantics
Invoked when applying targeted swaps to disrupt patterns while preserving equivalence.
domain assumption Excess error beyond the noise model is attributable to identifiable circuit fragments
Core premise enabling delta debugging to isolate abnormal patterns.

pith-pipeline@v0.9.0 · 5588 in / 1423 out tokens · 44202 ms · 2026-05-10T05:20:00.239182+00:00 · methodology

discussion (0)

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