从IceCube到IT-Sphere：用于银行IT根因分析的混合量子-经典GNN
From IceCube to IT-Sphere: A Hybrid Quantum-Classical GNN for Banking IT Root Cause Analysis
- University of Siena（锡耶纳大学）
- Istituto Nazionale di Fisica Nucleare (INFN)（意大利国家核物理研究所）
- IBM Consulting（IBM咨询）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出混合量子-经典GNN（HQ-RCA）用于银行IT根因分析，以DynEdge为骨干、VQC为分类头，在13k告警数据上与经典基线F1持平，并验证了无梯度读出在NISQ硬件上的可行性。
AI中文摘要：
我们提出了混合量子根因分析（HQ-RCA），这是一个基于工业实践的银行IT运维根因分析工作流，构建于混合量子图神经网络（QGNN）之上：以DynEdge（IceCube中微子重建GNN，我们称之为独立DynEdge）的经典骨干网络为基础，将其分类头替换为变分量子电路（VQC）。在一家欧洲主要银行13个月的匿名IT数据（13k告警集群）上，混合QGNN在$F_1$指标上与独立DynEdge（最强的经典基线）持平，而独立DynEdge在排序指标上领先。通过维度表达性分析（DEA）进行的读出敏感性和布局鲁棒性研究表明，量子可观测量的有效参数维度（秩）与$F_1$无显著相关性；因此我们保留最简单的读出$\u27e8Z_0\u27e9$（第一个量子比特上的泡利-$Z$期望值），其在部署布局中秩为1，将优化问题简化为可通过无梯度网格扫描求解的一维问题。在IBM Heron r2（无错误缓解）上的执行表明，这种无梯度读出在阈值重新校准后可在NISQ硬件上执行。
英文摘要：
We present Hybrid Quantum Root Cause Analysis (HQ-RCA), an industrially grounded workflow for root cause analysis in banking IT operations, built on a hybrid Quantum Graph Neural Network (QGNN): the classical backbone of DynEdge (the IceCube neutrino-reconstruction GNN, which we call standalone DynEdge), with its classification head replaced by a Variational Quantum Circuit (VQC). On 13 months of anonymised IT data (13k alarm clusters) from a major European bank, the hybrid QGNN matches standalone DynEdge -- the strongest classical baseline -- on $F_1$, while standalone DynEdge leads the ranking metrics. A readout-sensitivity and layout-robustness study, analysed via Dimensional Expressivity Analysis (DEA), shows that the effective parameter dimensionality (rank) of the quantum observable has no measurable correlation with $F_1$; we therefore keep the simplest readout $\langle Z_0\rangle$ (the Pauli-$Z$ expectation on the first qubit), which in the deployed layout is rank-1, collapsing optimisation to a 1-D problem solvable by a gradient-free grid scan. Execution on IBM Heron r2 (no error mitigation) shows this gradient-free readout is executable on NISQ hardware after threshold recalibration.
