arXiv：cs.LG· Pablo Herrero G\'omez, Antonio Jimeno Morenilla, David Mu\~noz-Hern\'andez, Higinio Mora Mora·· 4 小时前AI 评分33
从真实信号学到的共享结构中实现量子数据加载
Quantum data loading from the learned shared structure of real signals
AI 导读
研究人员提出一种量子原生数据加载器，只需学习一次数据集共享的低维结构，即可用一组固定电路加载所有信号。在五个公开数据集、七种视角下，它以同等门成本达到最强结构化加载器的目标，且每个信号所需数字量减少数倍；这些数字可从随机子集中推断。
正文
Abstract:Preparing quantum states from classical data can cost more than the computation they serve; most loaders tailor a circuit to each input. Here we show that the signals of a real dataset share structure that can be learned once and reused. Our quantum-native loader learns a low-dimensional description of a dataset and prepares every signal with one fixed circuit set by a few numbers. Across seven views of five public datasets it meets the targets of the strongest structured loader at equal gate cost with several times fewer numbers per signal. These numbers can be inferred from a random subset: in a preregistered blind replication the subset needed to come within ten per cent of full-signal accuracy stayed constant within a prespecified margin as signals grew sixteenfold, whereas the structured loader needed ever more. It declines what it cannot represent, covering fewer cases than that baseline and no electrocardiogram.
| Subjects: | Quantum Physics (quant-ph); Machine Learning (cs.LG); Computational Physics (physics.comp-ph) |
| Cite as: | arXiv:2610.06076 [quant-ph] |
| (or arXiv:2610.06076v2 [quant-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2610.06076 arXiv-issued DOI via DataCite |
Submission history
From: Pablo Herrero Gomez [view email]
[v1]
Mon, 5 Oct 2026 10:08:05 UTC (923 KB)
[v2]
Tue, 6 Oct 2026 12:28:42 UTC (924 KB)
来源：arXiv：cs.LG · arxiv.org