深度学习用于使用波波变换对WS2单层中的量子发射信号进行分类
Hossein Najafzadeh1, Zahra Raissi2,3, Shole Golmohammady4
1Department of Medical Bioengineering, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran.
Scientific reports
|November 22, 2025
概括
深度学习模型准确地分类WS2纳米泡的量子发射信号,达到高达99.4%的准确性. 这种方法提高了量子材料的特性和量子技术的光谱区分能力.
科学领域:
- 量子材料科学 量子材料科学
- 机器学习应用 机器学习应用
- 频谱学是一种光谱学.
背景情况:
- 从像WS2单层纳米泡这样的材料中特征化量子发射信号至关重要,但具有挑战性.
- 评估光谱区分能力是量子信息应用的关键.
研究的目的:
- 开发和评估深度学习模型来分类来自WS2单层纳米泡的量子发射信号.
- 为此分类任务评估不同卷积神经网络架构的性能.
主要方法:
- 量子发射信号经过预处理,并使用连续波纹转换 (CWT) 将其转换为RGB图像.
- 三个CNN架构 (ResNet50,VGG16,Xception) 被训练并使用五倍交叉验证进行评估.
- 在不同的光谱带组合中评估了分类准确性.
主要成果:
- 所有评估的模型都实现了高分类精度,VGG16达到99.4%的平均精度.
- 对光谱相距较远的频段观察到完美的准确性,而相邻的频段则提出了更大的挑战 (VGG16的96.5%).
- Xception展示了高的计算效率,在短短2个时代内汇聚.
结论:
- 深度学习与CWT相结合,为量子发射信号分类提供了一个强大的框架.
- 这种方法对量子光子学,密码学和传感有重大影响.
- 该研究通过转移学习解决了量子系统中的数据稀缺问题,为未来的量子技术开发铺平了道路.
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