有限射击预算下量子电路 Born 机的噪声正则化训练
Noise-regularized training of quantum circuit Born machines under a finite shot budget
AI 导读
研究者提出 GENIE 框架，在固定执行预算下通过带误差缓解上限的噪声水平选择来训练量子电路 Born 机（QCBM）。理论证明噪声可作为正则化手段，任意噪声下最大均值差异的泛化间隙由噪声模型可达分布的复杂度界定。合成与人口普查数据模拟显示，GENIE 表现接近四种噪声控制策略中的最优者，而任一固定噪声水平在多种场景下都不及它。
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