From IceCube to IT-Sphere: A Hybrid Quantum-Classical GNN for Banking IT Root Cause Analysis

Antonio Greco; Riccardo Paoletti; Roberto Cappuccio; Mario Onorato

quant-phSep 19, 2026

From IceCube to IT-Sphere: A Hybrid Quantum-Classical GNN for Banking IT Root Cause Analysis

Authors: Antonio Greco, Riccardo Paoletti, Roberto Cappuccio, Mario Onorato

Organizations: Department of Physical Sciences, Earth and Environment (DSFTA), University of Siena, Siena, Italy · Istituto Nazionale di Fisica Nucleare (INFN), Pisa, Italy · IBM Consulting—Promontory, IBM Industry Diamond, Banking & Financial Markets, Milan, Italy

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.

Figures & tables

Panel of Figure 1 — Fig. 1: IceCube $\leftrightarrow$ IT-Sphere structural correspondence: each centre concept links a neutrino-detector notion (left) to its banking-IT counterpart (right) — a structural analogy, not a task identity.

Panel of Figure 2 — Fig. 2: HQ-RCA end-to-end workflow: a quantum block ( $\boldsymbol{\theta}{\to}\langle Z_{0}\rangle$ ) feeding a classical block ( $\langle Z_{0}\rangle{\to}F_{1}$ ). AP and ROC-AUC are read off $\mathbb{P}(y|x_{v})$ directly (threshold-free).

Panel of Figure 3 — Fig. 3: Left: DEA SVD spectrum (log) — only $\sigma_{1}{=}1.336$ exceeds $\varepsilon{=}10^{-3}$ . Right: $F_{1}(\theta_{0})$ from a 40-point grid scan, a single broad plateau ( $\sim$ 37% of period) peaking at $\theta_{0}^{*}{=}6.241$ rad ( $\bigstar$ ).

Panel of Figure 4 — Fig. 4: $F_{1}$ , AP, ROC-AUC for all models (2,000-graph subset; AP chance ${\approx}0.17$ ); DEA rank below the quantum readouts. Error bars: $\sigma$ , three held-out subsets (HW: five folds).

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Quantum Convolutional Neural Networks Quantum-Inspired ML Neural Network Interpretability Medical Image Classification Explainable Medical Image Analysis

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Numerous studies have analyzed the use of hybrid quantum-classical convolutional neural networks as a promising alternative to classical deep learning. However, network components on quantum hardware impose fundamental limitations, while the scalability of quantum circuits leads to trainability issues. In this work, we investigate whether small, classically-emulated quantum circuit components can play a meaningful role within complex models, offering an alternative to purely classical convolutional architectures. To this end, we present a systematic study of the effectiveness of a Hybrid Quantum-inspired Convolutional Neural Network (HQiCNN) compared with a parameter-matched classical Convolutional Neural Network (CNN) that differs only in an intermediate dense neural layer. Both models are evaluated on two real-world medical datasets while systematically varying the different hyperparameters, ensuring a fair model comparison that is both dataset and hyperparameter independent. The results show that no architecture consistently dominates the other: the HQiCNN achieves its largest gains in intermediate-data regimes, whereas the CNN reaches the highest accuracies for the largest training sets in both datasets. Furthermore, removing entanglement produces comparable performance while enabling substantially better scalability of quantum simulations, and richer observable sets become beneficial only when sufficient training data are available. Finally, we propose two SHAP-based explainability tools for comparing the predictions between both models,

|SHAP|

IoU and

EMD_{pos}

metric, to demonstrate that both architectures consistently attend to anatomically plausible regions. Thus, we provide a comprehensive benchmark showing that, under certain conditions, hybrid quantum-inspired models are an alternative that can offer benefits in practical tasks such as medical image classification.

Abstract

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