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机器学习对量子误差缓解的方法,以获得准确的分子能量.

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本研究引入了一种机器学习方法,用于减少化学量子计算中的噪音. 它显著改善了分子的能量预测,使量子算法变得更加实用.

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科学领域:

  • 量子计算是一种量子计算.
  • 计算化学计算化学
  • 机器学习 机器学习

背景情况:

  • 混合量子-经典算法受到硬件噪声的限制,阻碍了超越原理证明的实际应用.
  • 现有的误差缓解 (EM) 技术与用于复杂分子模拟的杂中级量子 (NISQ) 设备作斗争.

研究的目的:

  • 开发一种机器学习 (ML) 架构,用于模拟分子哈密尔顿的实际误差缓解 (EM).
  • 在化学量子计算中克服当前电磁技术和硬件噪声的局限性.

主要方法:

  • 为EM设计了一个图形神经网络和基于回归的ML架构.
  • 该ML模型在浅子电路上使用理想或减轻的预期值进行训练.
  • 硬件连接被映射到一个定向图中,为特征生成编码本地门噪声配置文件.

主要成果:

  • 拟议的ML架构实现了预测分子能量的数量级改进.
  • 在分子之间证明有效性,它们的解离能量配置文件的相关性各不相同.
  • 该方法避免了通常与EM技术相关的指数式开销.

结论:

  • 这种基于ML的EM方法为利用NISQ设备在分子模拟中提供了一个实用的途径.
  • 该策略有效地学习和减轻硬件噪声,提高量子计算的准确性.
  • 这些发现为在当前量子硬件上进行更可靠的量子化学计算铺平了道路.