Coupling-Grouped XY-QAOA for Joint Anomaly-Feature Selection
Abstract
Selecting anomalous samples and explanatory features under fixed cardinalities defines a coupled optimization problem: feature-first selection can overlook features whose usefulness depends on the selected samples. In the analyzed model, joint calibration-error sensitivity is sample-count independent, while the sum-column feature-first rule's sensitivity grows linearly. To optimize this formulation, we introduce Coupling-Grouped XY Quantum Approximate Optimization Algorithm (CG-XY-QAOA), with constraint-preserving mixers and block-structured phase angles. On matched sparse IBM Heron R3 circuits, our implementation reduces circuit depth by 35.1%-54.8% against Qiskit's optimization-level 3 preset pass manager on the CZ-basis target and enables runs at 64 decision qubits (p=2) and 36 (p=3). At 20 decision qubits (p=5), fully grouped angles raise the fixed low-energy hit probability 25-fold to 3.7%, with 64.7% of measured samples satisfying both constraints. Benchmarks favor joint selection when feature usefulness varies across anomalies. Noiseless simulations show problem-structured grouping improves over same-depth XY-QAOA and parameter-matched type-preserving randomizations.
Related
- Pareto-optimal quantum kernel selection for unsupervised anomaly detection on real malware beaconing data
- Feature tracking in physics-informed neural networks via joint optimization of nonlinear deformation manifolds: application to shocks
- Landscape-Dependent Performance of Photonic Quantum Solvers in QUBO Feature Selection for Financial Risk Detection
- Neural LoFi with Backward Coupling: A Spectral Theory of Cross-Layer Feature Learning
- Quantum Feature Selection for Biomedical Data Analysis
BibTeX
@misc{taipale2026couplinggrouped,
title = {{Coupling-Grouped XY-QAOA for Joint Anomaly-Feature Selection}},
author = {Pauli Taipale},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2606.13244},
eprint = {2606.13244},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.13244},
}
RIS
TY - UNPB
TI - Coupling-Grouped XY-QAOA for Joint Anomaly-Feature Selection
AU - Pauli Taipale
PY - 2026
DA - 2026/09/01
PB - arXiv
DO - 10.48550/arxiv.2606.13244
UR - https://arxiv.org/abs/2606.13244
AB - Selecting anomalous samples and explanatory features under fixed cardinalities defines a coupled optimization problem: feature-first selection can overlook features whose usefulness depends on the selected samples. In the analyzed model, joint calibration-error sensitivity is sample-count independent, while the sum-column feature-first rule's sensitivity grows linearly. To optimize this formulation, we introduce Coupling-Grouped XY Quantum Approximate Optimization Algorithm (CG-XY-QAOA), with constraint-preserving mixers and block-structured phase angles. On matched sparse IBM Heron R3 circuits, our implementation reduces circuit depth by 35.1%-54.8% against Qiskit's optimization-level 3 preset pass manager on the CZ-basis target and enables runs at 64 decision qubits (p=2) and 36 (p=3). At 20 decision qubits (p=5), fully grouped angles raise the fixed low-energy hit probability 25-fold to 3.7%, with 64.7% of measured samples satisfying both constraints. Benchmarks favor joint selection when feature usefulness varies across anomalies. Noiseless simulations show problem-structured grouping improves over same-depth XY-QAOA and parameter-matched type-preserving randomizations.
ER -