A Multi-Level Integrity Evaluation Framework for Quantum Circuits under Controlled Anomaly Injection

TL;DR

This paper proposes a three-layer metric framework combining structural, behavioral, and interaction-level analyses to evaluate quantum circuit integrity, addressing limitations of existing methods in the NISQ era.

Contribution

It introduces a novel multi-level evaluation framework with three complementary metrics, improving quantum circuit validation under various anomalies.

Findings

OIS detects 93.85% of anomalies in structural blind-spot cases.

IGS detects 72.58% of anomalies, complementing other metrics.

Metrics together provide a comprehensive assessment of circuit deviations.

Abstract

Ensuring the integrity of quantum circuits is a significant challenge in the Noisy Intermediate-Scale Quantum (NISQ) era, where circuits are subject to compilation transformations, hardware constraints, and potential adversarial modifications. Existing validation approaches typically rely on either structural analysis or behavioral evaluation, leading to incomplete assessment of circuit correctness. In this work, we investigate the relationship between structural, interaction-level, and behavioral perspectives of circuit integrity, demonstrating that a single aspect of integrity is insufficient to guarantee circuit integrity; structural similarity alone does not ensure behavioral equivalence. To address this problem, we use a three-layer metric framework that combines the Structural Integrity Score (SIS), the Operational Integrity Score (OIS), and the Interaction Graph Semantic-Logical Score (IGS). SIS captures global structural properties, OIS quantifies behavioral divergence using Jensen-Shannon distance, and IGS models interaction patterns and dependencies in a pre-execution setting. Through controlled anomaly injection on benchmark quantum circuits, we demonstrate that each metric captures a different aspect of circuit deviation. In particular, structural blind-spot cases (SIS >= 0.95) reveal a clear limitation of structural analysis, where OIS detects anomalies in 93.85% of instances, while IGS detects 72.58%. These results highlight that the metrics provide complementary insights and that a single metric is insufficient for reliable circuit validation.