QIBONN: A Quantum-Inspired Bilevel Optimizer for Neural Networks on Tabular Classification
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912703972376576 |
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| author | Chumpitaz-Flores, Pedro Duong, My Mao, Ying Hua, Kaixun |
| author_facet | Chumpitaz-Flores, Pedro Duong, My Mao, Ying Hua, Kaixun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_08940 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | QIBONN: A Quantum-Inspired Bilevel Optimizer for Neural Networks on Tabular Classification Chumpitaz-Flores, Pedro Duong, My Mao, Ying Hua, Kaixun Machine Learning Quantum Physics 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. |
| title | QIBONN: A Quantum-Inspired Bilevel Optimizer for Neural Networks on Tabular Classification |
| topic | Machine Learning Quantum Physics |
| url | https://arxiv.org/abs/2511.08940 |