---
id: 20260302-T0-15
title: "量子机器学习的长程频率调优技术"
title_en: "Long-Range Frequency Tuning in Quantum ML"
url: https://ai.daily.yangsir.net/daily/20260302-T0-15
issue_date: 2026-03-02
publish_date: 2026-03-02T05:00:00.000Z
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2602.23409
---

# 量子机器学习的长程频率调优技术

该研究提出量子机器学习中的长程频率调优方法。通过优化角度编码的傅里叶级数截断，显著降低了量子电路深度需求。实验表明，该方法将参数规模减少至O(ω)级别，同时保持通用函数逼近能力。适用于资源受限的量子计算设备，可提升QML模型训练效率。

## English Version

**Long-Range Frequency Tuning in Quantum ML**

This research proposes a long-range frequency tuning method for quantum machine learning. By optimizing Fourier series truncation of angle encoding, it significantly reduces quantum circuit depth requirements. Experiments show the method reduces parameter complexity to O(ω) while maintaining universal function approximation capability. It enhances QML model training efficiency on resource-constrained quantum computing devices.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2602.23409)

**详情页**：https://ai.daily.yangsir.net/daily/20260302-T0-15

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