通过量子回火来解决机器学习的希格斯优化问题
Alex Mott1, Joshua Job2,3, Jean-Roch Vlimant1
1Department of Physics, California Institute of Technology, Pasadena, California 91125, USA.
Nature
|October 21, 2017
概括
量子和经典回火方法被用来改进机器学习的希格斯玻色子衰变检测. 这些基于回火的新型分类器的性能与当前的方法相美,并为小型数据集提供优势.
科学领域:
- 高能物理
- 粒子物理中的机器学习应用
- 量子计算和回火
背景情况:
- 机器学习对于在标准模型过程中识别希格斯玻色子衰变至关重要.
- 目前的方法依赖于可能引入标签噪声和系统错误的模拟.
- 过度培训和培训数据相关性的错误是一个重大挑战.
研究的目的:
- 将量子和古典化应用于希格斯信号与背景机器学习优化问题.
- 开发一个强大的分类器,适应模拟的缺陷.
- 将基于回火的分类器的性能与最先进的方法进行比较
主要方法:
- 将机器学习问题映射到Ising旋转模型的基本状态.
- 使用基于希格斯衰变光子运动可观测的弱分类器构建了强分类器.
- 使用量子和古典火技术进行优化.
主要成果:
- 基于 Annealing 的分类器的性能与当前最先进的机器学习方法相美.
- 分类器是可解释的实验参数的简单函数.
- 对于小型训练数据集来说,它表现出了比传统方法的优势.
结论:
- 量子和经典化为粒子物理学分类提供了强大而可解释的替代方案.
- 该技术的简单性和对错误的适应性表明它在实验粒子物理学中具有广泛的适用性.
- 潜在的应用包括实时事件选择和中微子物理分类.
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