有机城市固体废物分类的量子灵感深度学习模型,朝着循环生物经济的方向发展
Nathimalar Chandran1, N Ramesh Babu1
1Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India.
Journal of environmental management
|March 12, 2026
概括
这项研究介绍了量子生物网2.0,一种混合量子-经典模型来分类有机废物. 它在区分有机废物和无机废物以及细粒亚类方面实现了高精度,改善了废物管理和资源回收.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 量子计算是一种量子计算.
背景情况:
- 有机废物是城市固体废物的重要组成部分,需要有效的来源隔离,以有效地处理废物和资源回收.
- 对传统的深度学习模型来说,区分视觉上相似的有机废物子类是一个挑战.
- 准确的分类对于自动化废物分类系统至关重要,以提高材料回收和可持续性.
研究的目的:
- 引入量子生物网2.0,一种用于结构化有机废物分类的新型混合量子-经典框架.
- 克服传统深度学习模型在区分视觉上相似的有机废物类别方面的局限性.
- 提高源级有机废物分离的效率和准确性,以提高资源回收.
主要方法:
- 开发了量子生物网2.0,将ResNet50特征提取与经典密度层和八量子位变量量子电路集成在一起.
- 采用两阶段的层次分类方法:二元分类 (有机/无机),然后细粒度分类有机废物子类.
- 在9000张废物图像的精选数据集上训练和评估框架,将性能与最先进的经典模型进行比较.
主要成果:
- 第1阶段在有机/无机废物二元分类中实现了99.44%的准确性,超过了几种领先的经典模型.
- 第二阶段在细粒度有机废物分类方面获得了98.35%的准确性和99.96%的AUC,超过了经典的基线.
- 混合量子-经典方法在细粒度分类中与纯经典卷积模型相比,显示出可测量的性能增长.
结论:
- 结构化混合量子-经典特征融合为细粒度有机废物分类提供了显著的性能改进.
- 量子生物网2.0有效地支持源级废物分离,并帮助自动分类系统.
- 该框架显示了增强材料回收和推进可持续废物管理实践的前景.
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