发表机构
Universidad de Alicante（阿利坎特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种量子原生加载器，通过一次性学习数据集共享的低维结构，用固定电路高效制备所有信号，在多个数据集上以更少参数达到同等性能，并支持随机子集推断。
AI 中文摘要
从经典数据制备量子态的成本可能超过其服务的计算本身；大多数加载器为每个输入定制电路。这里我们表明，真实数据集的信号共享结构可以被学习一次并重复使用。我们的量子原生加载器学习数据集的低维描述，并用一个由少数数字设定的固定电路制备每个信号。在五个公共数据集的七个视图上，它以相同的门成本达到了最强结构化加载器的目标，而每个信号所需的数字却少了几倍。这些数字可以从随机子集中推断出来：在一项预注册的盲复制中，当信号增长十六倍时，所需子集以在预设裕度内保持恒定，以达到全信号准确度的百分之十以内，而结构化加载器则需要更多的数字。它拒绝无法表示的内容，覆盖的案例少于该基线，且不包含心电图。
英文摘要
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.
