混合量子机器学习架构的设计和量子噪声效应的分析
J A Bravo-Montes1,2, A Martín-Toledano3, C Velasco-Gallego4
1HPC & Quantum, Bull Iberia, Madrid, 28037, Spain. andres.bravo@bull.com.
Scientific reports
|March 14, 2026
概括
本研究介绍了一种混合量子-经典架构,用于噪音量子机器学习 (QML). 结果表明噪音可以简化QML模型,降低计算成本,以最小的准确性损失.
科学领域:
- 量子计算是一种量子计算.
- 机器学习 机器学习
- 计算科学 计算科学
背景情况:
- 量子机器学习 (QML) 为复杂问题提供解决方案,但受到量子设备中的噪声的阻碍.
- 开发强大的QML模型需要解决环境噪声对量子计算的影响.
研究的目的:
- 为在噪音条件下训练QML模型提供混合量子-经典架构.
- 评估噪声对超导量子系统的影响及其对QML模型复杂性和性能的影响.
主要方法:
- 模拟杂的超导量子系统以评估噪声影响.
- 开发和测试QML的混合量子-经典架构.
- 将架构应用于用于乳腺癌检测的生物医学数据集.
主要成果:
- 某些噪声水平可以简化量子神经网络架构,降低量子位需求和计算成本.
- 对于乳腺癌检测数据集,无噪声和噪声配置之间的精度差异是最小的 (0.11%1.68%).
- 在训练过程中将噪声纳入训练过程中,在Qaptiva 802模拟器上,提高了1.614.39的执行时间.
结论:
- 拟议的混合架构有效地在杂的环境中训练QML模型.
- 可以利用噪音来优化QML模型,减少复杂性并提高训练效率,而不会显著降低准确性.
- 这种方法对生物医学数据分析等领域的实际QML应用具有前景.
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