arXiv cs.AI· Hoang M. Ngo, Quan Nguyen, Jung Taek Seo, Wanli Xing, My T. Thai·· 11 小时前AI 评分20
有限采样预算下量子电路Born机器的噪声正则化训练
Noise-regularized training of quantum circuit Born machines under a finite shot budget
AI 导读
研究证明，用最大均值差异训练的量子电路Born机器（QCBM），其泛化差距受噪声模型可达分布复杂度约束；加扰的局部Pauli噪声会压缩该复杂度，但使模型偏向均匀分布，因此残余噪声可充当正则项且最优强度可能是中间值。
正文 · 原文
Abstract:Quantum circuit Born machines (QCBMs) can learn distributions that are hard to sample from classically, but their value depends on generalizing from finite training data. Here, we study how hardware noise affects this generalization. We show that, for any noise process, the generalization gap of a QCBM trained with the maximum mean discrepancy is bounded by the complexity of the distributions the noisy model can reach. Scrambled local Pauli noise contracts this complexity but biases the model towards the uniform distribution, so residual noise acts as a regularizer whose optimal strength can be intermediate. We propose GENIE, a framework that realizes a chosen noise level with capped error mitigation and selects it during training from held-out data within a fixed execution budget, with provably bounded regret. In simulations on synthetic and census data, GENIE stays close to the best of four noise-control strategies, whereas every fixed noise level falls short in many settings.
| Subjects: | Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2611.06630 [quant-ph] |
| (or arXiv:2611.06630v1 [quant-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2611.06630 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Hoang Ngo [view email]
[v1]
Wed, 4 Nov 2026 15:13:20 UTC (1,094 KB)
来源：arXiv cs.AI · arxiv.org