混合量子神经网络模型用于水果质量评估
Danish Ul Khairi1, Kamran Ahsan1, Syed Zeeshan Ali1
1Department of Computer Science, Federal Urdu University of Arts Science and Technology, Karachi, Pakistan.
PloS one
|December 10, 2025
概括
混合量子神经网络在水果质量评估方面表现有前途. 与受控NOT (CNOT) 门相比,受控阶段 (CZ) 门架构表现出更稳定的训练和竞争精度.
科学领域:
- 量子计算是一种量子计算.
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 混合量子神经网络 (HQNNs) 正在成为复杂模式识别任务的强大工具.
- 评估水果质量往往需要复杂的模型,能够处理各种数据特征.
研究的目的:
- 调查纠门选择对水果质量评估HQNN绩效的影响.
- 为了比较两个HQNN架构:NNQEv1 (CNOT门) 和NNQEv2 (CZ门).
主要方法:
- 开发了两个HQNN架构 (NNQEv1和NNQEv2),使用不同的纠门 (CNOT与CZ).
- 基于网关分解和硬件噪声考虑的理论分析架构.
- 在经典硬件上进行计算执行的量子电路,用于性能评估.
- 将HQNN模型与经典和深度学习基准进行比较.
主要成果:
- 这两种HQNN模型都实现了高测试准确度:98.7% (MNIST),98.6% (FruitQ) 和96.7% (定制果数据集).
- 基于CZ的NNQEv2表现出比基于CNOT的NNQEv1更稳定的培训动态和更紧密的交叉验证置信区间.
- 结果支持了CZ-gate架构稳定的理论预测.
结论:
- 门级设计选择显著影响HQNN的稳定性和性能.
- 基于CZ-gate的HQNN架构是强大的量子机器学习应用程序的一个有希望的方向.
- 这项研究为开发先进的量子机器学习算法提供了基础的见解.
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