论文标题
带有纠缠嵌入的量子语言模型，以回答问题
Quantum Language Model with Entanglement Embedding for Question Answering
论文作者
论文摘要
将单词建模为量子叠加的量子语言模型（QLM）证明了高水平的模型透明度和良好的事后解释性。然而，在当前的文献中，基本上将单词序列建模为单词状态的经典混合物，该序列无法完全利用量子概率描述的潜力。完整的量子模型尚待开发，以明确捕获单词序列中的非经典相关性。我们提出了一个具有新颖的纠缠嵌入（EE）模块的神经网络模型，该模块的功能是将单词序列转换为多体量子系统的纠缠纯净状态。强量子纠缠是量子信息的核心概念，也是单词之间平行相关性的指示，在单词序列中观察到。数值实验表明，与经典的深度神经网络模型和其他问题答录（QA）数据集相比，使用EE（QLM-EE）提出的QLM实现了卓越的性能。此外，可以通过量化单词之间的纠缠程度来提高模型的事后解释性。
Quantum Language Models (QLMs) in which words are modelled as quantum superposition of sememes have demonstrated a high level of model transparency and good post-hoc interpretability. Nevertheless, in the current literature word sequences are basically modelled as a classical mixture of word states, which cannot fully exploit the potential of a quantum probabilistic description. A full quantum model is yet to be developed to explicitly capture the non-classical correlations within the word sequences. We propose a neural network model with a novel Entanglement Embedding (EE) module, whose function is to transform the word sequences into entangled pure states of many-body quantum systems. Strong quantum entanglement, which is the central concept of quantum information and an indication of parallelized correlations among the words, is observed within the word sequences. Numerical experiments show that the proposed QLM with EE (QLM-EE) achieves superior performance compared with the classical deep neural network models and other QLMs on Question Answering (QA) datasets. In addition, the post-hoc interpretability of the model can be improved by quantizing the degree of entanglement among the words.
