Zhenyu Qi

Zhenyu Qi, Qian Zhang, Haotang Li et al.

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 $ρ$ = 0.515, p = 0.0007) and 45% on ibm_marrakesh ($ρ$ = 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.

Haotang Li, Zhenyu Qi, Hao Qin et al.

Self-supervised semantic segmentation methods often fail when faced with appearance ambiguities. We argue that this is due to an over-reliance on unstable, appearance-based features such as shadows, glare, and local textures. We propose \textbf{GASeg}, a novel framework that bridges appearance and geometry by leveraging stable topological information. The core of our method is Differentiable Box-Counting (\textbf{DBC}) module, which quantifies multi-scale topological statistics from two parallel streams: geometric-based features and appearance-based features. To force the model to learn these stable structural representations, we introduce Topological Augmentation (\textbf{TopoAug}), an adversarial strategy that simulates real-world ambiguities by applying morphological operators to the input images. A multi-objective loss, \textbf{GALoss}, then explicitly enforces cross-modal alignment between geometric-based and appearance-based features. Extensive experiments demonstrate that GASeg achieves state-of-the-art performance on four benchmarks, including COCO-Stuff, Cityscapes, and PASCAL, validating our approach of bridging geometry and appearance via topological information.