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arXiv · 2511.08940

QIBONN: A Quantum-Inspired Bilevel Optimizer for Neural Networks on Tabular Classification

Pedro Chumpitaz-Flores·My Duong·Ying Mao·Kaixun Hua
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Abstract

Hyperparameter optimization (HPO) for neural networks on tabular data is critical to a wide range of applications, yet it remains challenging due to large, non-convex search spaces and the cost of exhaustive tuning. We introduce the Quantum-Inspired Bilevel Optimizer for Neural Networks (QIBONN), a bilevel framework that encodes feature selection, architectural hyperparameters, and regularization in a unified qubit-based representation. By combining deterministic quantum-inspired rotations with stochastic qubit mutations guided by a global attractor, QIBONN balances exploration and exploitation under a fixed evaluation budget. We conduct systematic experiments under single-qubit bit-flip noise (0.1\%--1\%) emulated by an IBM-Q backend. Results on 13 real-world datasets indicate that QIBONN is competitive with established methods, including classical tree-based methods and both classical/quantum-inspired HPO algorithms under the same tuning budget.

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Pedro Chumpitaz-Flores, My Duong, Ying Mao, Kaixun Hua. 2025-11-12. QIBONN: A Quantum-Inspired Bilevel Optimizer for Neural Networks on Tabular Classification. https://arxiv.org/abs/2511.08940

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