碳效率量子AI:在QNN和QLSTM模型中对安赛特设计权衡的实证研究
Sarvapriya Tripathi1, Himanshu Upadhyay2, Jayesh Soni3
1Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA. strip011@fiu.edu.
Scientific reports
|December 30, 2025
概括
量子机器学习 (QML) 为绿色人工智能提供了潜在的途径,但当前的量子模型显示的能源成本高于经典方法. 简单的量子神经网络 (QNN) 设计比复杂的更节能,但经典模型在速度和能源使用方面仍然优于QML.
科学领域:
- 计算机科学 计算机科学
- 量子计算是一种量子计算.
- 人工智能的人工智能
背景情况:
- 深度学习对环境的影响需要可持续的计算解决方案,如绿色人工智能.
- 量子机器学习 (QML) 为传统人工智能提供了一个潜在的节能替代方案.
研究的目的:
- 为了对量子神经网络 (QNN) 和量子长期短期记忆 (QLSTM) 进行能源效率和性能的基准测试.
- 将QML模型与经典机器学习算法 (ANN,LSTM,CatBoost) 进行比较,并评估量子硬件与仿真之间的训练.
主要方法:
- 在N-BaIoT数据集上对10种不同的量子电路设计 (ansätze) 进行QNN和QLSTM的比较.
- 将量子模型与经典的人工神经网络 (ANN),长短期记忆 (LSTM) 和CatBoost模型进行比较.
- 在模拟环境和实际量子硬件上评估训练量子模型的能量消耗和执行时间.
主要成果:
- 简单的QNN方法 (例如,A4) 实现了与复杂方法相比的准确性,能耗显著降低,融合速度更快.
- 与QML模型相比,经典的机器学习模型表现出更快的训练时间和更低的能源消耗.
- 对实际量子硬件的培训比仿真产生了更高的能源成本,这表明需要改善基础设施.
结论:
- 虽然QML显示了通过优化电路设计提高能源效率的承诺,但经典ML目前提供了更高的性能和更低的能源消耗.
- 量子硬件,基础设施和硬件意识的替代优化方面的进一步进展对于实现可扩展,碳效率高的QML至关重要.
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