量子机器学习算法的古典数据:广度编码和和语言模糊性之间的关系
Jurek Eisinger1, Ward Gauderis1, Lin de Huybrecht1
1Computational Creativity Lab, Vrije Universiteit Brussel, Pleinlaan 9, 1050 Elsene, Belgium.
分类组合分布式 (DisCoCat) 模型使用量子状态来定义单词的含义. 广度编码经典数据增强了量子自然语言处理模型,并澄清了和句子模两可之间的联系.
科学领域:
- 量子计算是一种量子计算.
- 计算语言学 计算语言学
- 自然语言处理自然语言处理.
背景情况:
- 分类组合分布式 (DisCoCat) 模型将单词含义表示为量子状态,通过语法相互作用.
- 该模型已使用密度矩阵扩展,以解决语言模两可的问题.
- 像·诺伊曼和忠实度这样的测量量这些密度矩阵的混合性.
研究的目的:
- 调查广度编码经典数据在自然语言处理量子机器学习中的影响.
- 探索振幅编码数据如何影响密度矩阵混合 () 和语言模两可之间的关系.
主要方法:
- 使用振幅编码将经典数据引入量子机器学习算法.
- 应用·诺伊曼和忠实度作为混合度的尺度,在表示句子的密度矩阵中.
- 分析与编码数据的-模糊性关系的解释性.
主要成果:
- 数据的幅度编码可以提高量子机器学习模型在量子自然语言处理中的性能.
- 该研究提供了关于编码的古典数据如何影响和模两可之间的联系的见解.
- 输入力-模两可关系的解释性通过振幅编码显著增强.
结论:
- 广度编码是改进量子自然语言处理模型的一个有价值的技术.
- 经典数据编码使密度矩阵和句子模两可之间的关系更加直观地可理解.
- 这项研究将量子力学,语言学和机器学习联系起来,以增强语言理解.
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