在量子神经网络中进行比较分析和噪声强度评估
Tasnim Ahmed1,2, Muhammad Kashif1,2, Alberto Marchisio3,4
1eBrain Lab, Division of Engineering, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
Scientific reports
|September 29, 2025
概括
混合量子神经网络 (HQNN) 显示出对杂量子设备的前景. 这项研究发现,与图像分类的其他HQNN算法相比,量子神经网络 (QuanNN) 对量子噪声提供了更高的稳定性.
科学领域:
- 量子计算是一种量子计算.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 噪音中等量级量子 (NISQ) 设备对混合量子神经网络 (HQNN) 提出了挑战.
- 量子噪声在实际应用中显著影响HQNN的性能.
- 图像分类是正在探索HQNN的一个关键任务.
研究的目的:
- 为图像分类进行不同HQNN算法的比较分析.
- 评估量子卷积神经网络 (QCNN),量子卷积神经网络 (QuanNN) 和量子转移学习 (QTL) 的性能和噪声强度.
- 确定最佳架构并评估它们对各种量子噪声通道的弹性.
主要方法:
- 对图像分类的QCNN,QuanNN和QTL算法的比较分析.
- 对量子电路中的算法进行评估,具有不同的纠结构和层数.
- 使用阶段翻转,比特翻转,阶段缓解,振幅缓解和去极化通道噪声模型评估噪声强度.
主要成果:
- 性能最高的HQNN模型对不同的量子噪声通道表现出不同的弹性.
- QuanNN 始终优于其他车型,在各种噪音类型中表现出更高的稳定性.
- 选择HQNN架构至关重要,应考虑NISQ设备的特定噪声环境.
结论:
- QuanNN成为一个更强大的HQNN算法,用于NISQ设备上的图像分类.
- 根据量子硬件的特定噪声特性定制HQNN模型选择对于最佳性能至关重要.
- 对HQNNs的噪音减轻策略进行进一步的研究是有必要的.
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