用量子启发方法优化神经网络超参数，提升表格数据分类效果
QIBONN: A Quantum-Inspired Bilevel Optimizer for Neural Networks on Tabular Classification
- 将特征选择与模型结构统一用量子比特表示，实现多维超参联合优化
- 在13个真实数据集上表现媲美经典与量子启发方法，且评估成本更低
- 适合需要高效调参的表格数据建模场景，尤其关注计算资源受限时
针对表格数据上的神经网络超参数优化（HPO）难题，本文提出量子启发双层优化器QIBONN。该方法将特征选择、网络架构超参和正则化统一编码为量子比特表示，结合确定性量子启发旋转与由全局吸引子引导的随机比特突变，在固定评估预算下平衡探索与利用。通过模拟IBM-Q后端的单量子比特翻转噪声（0.1%–1%），在13个真实世界数据集上进行系统实验，结果表明QIBONN在相同调参预算下性能可媲美经典树基方法及各类经典/量子启发式HPO算法。
原文摘要 · Abstract (English)
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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