QAmplifyNet:使用可解释的混合量子-经典神经网络推动供应链后期订单预测的边界
Md Abrar Jahin1, Md Sakib Hossain Shovon2, Md Saiful Islam1
1Department of Industrial Engineering and Management, Khulna University of Engineering and Technology (KUET), Khulna, 9203, Bangladesh.
Scientific reports
|October 25, 2023
概括
本研究介绍了QAmplifyNet,一个量子经典的神经网络,用于准确的供应链后期订单预测,特别是在具有挑战性的不平衡数据集上. 这种新型模式显著改善了库存管理和运营效率.
科学领域:
- 人工智能的人工智能
- 量子计算是一种量子计算.
- 供应链管理 供应链管理
背景情况:
- 准确的后期订单预测对于供应链优化,库存控制和客户满意度至关重要.
- 传统的机器学习模型在供应链中常见的大型复杂数据集的局限性.
- 现有的方法与短暂和不平衡的数据集扎,阻碍了有效的后期预测.
研究的目的:
- 通过使用量子启发技术,引入供应链后期订单预测的新方法框架.
- 开发和评估量子经典神经网络QAmplifyNet,以获得更高的后期订单预测准确性.
- 为了应对在短暂和不平衡的数据集上预测后期订单的挑战.
主要方法:
- 在量子古典神经网络 (QAmplifyNet) 中利用量子启发的技术.
- 评估了七种预处理技术,使用后勤回归选择了最佳的技术.
- 利用可解释的人工智能技术来提高模型的解释性.
主要成果:
- 在QAmplifyNet中,比起经典模型,量子集合和深度强化学习,QAmplifyNet表现出卓越的性能.
- 获得了高的F1分数: 94%的"非后期订单"和75%的"后期订单".
- 显示了最高的AUC-ROC得分79.85%,证实了强大的预测能力.
结论:
- QAmplifyNet在供应链后期订单预测方面取得了突破,特别是在不平衡的数据集方面.
- 该模型增强了库存控制,降低了成本,提高了运营效率.
- 这项研究强调了量子启发机器学习在供应链管理中的潜力.
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