QIBONN: A Quantum-Inspired Bilevel Optimizer for Neural Networks on Tabular Classification
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.
Cite
@article{arxiv.2511.08940,
title = {QIBONN: A Quantum-Inspired Bilevel Optimizer for Neural Networks on Tabular Classification},
author = {Pedro Chumpitaz-Flores and My Duong and Ying Mao and Kaixun Hua},
journal= {arXiv preprint arXiv:2511.08940},
year = {2025}
}
Comments
6 pages, 3 figures, 3 tables. Accepted at IEEE International Conference on Big Data 2025