Problem
Research questions and friction points this paper is trying to address.
Hybrid Quantum-Classical GNN
Root Cause Analysis
Banking IT Operations
From IceCube to IT-Sphere: A Hybrid Quantum-Classical GNN for Banking IT Root Cause Analysis
🤖 AI Summary
提出了一种结合量子图神经网络的混合模型HQ-RCA，用于银行IT运维中的根因分析，并在实际数据上验证了其有效性。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Hybrid Quantum-Classical GNN
Root Cause Analysis
Variational Quantum Circuit
Dimensional Expressivity Analysis
NISQ Hardware
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