标题： 量子条件随机场 显示英文标题

标题： Quantum Conditional Random Field

摘要： 条件随机场（CRF）是一种用于标记序列数据的重要概率机器学习模型，广泛应用于自然语言处理、生物信息学和计算机视觉。然而，当处理大规模训练样本时，训练CRF模型在计算上是难以处理的。由于在量子环境下对序列数据标记的研究工作很少，本文通过引入定义良好的哈密顿量和测量方法构建了一个量子CRF（QCRF）模型，并提出了一种训练该模型的量子算法。结果表明，该算法在其经典对应算法上实现了指数级加速。此外，我们还证明了QCRF模型的Vapnik Chervonenkis维数高于经典CRF模型，这意味着QCRF具有更高的学习能力。 显示英文摘要

摘要： Conditional random field (CRF) is an important probabilistic machine learning model for labeling sequential data, which is widely utilized in natural language processing, bioinformatics and computer vision. However, training the CRF model is computationally intractable when large scale training samples are processed. Since little work has been done for labeling sequential data in the quantum settings, we in this paper construct a quantum CRF (QCRF) model by introducing well-defined Hamiltonians and measurements, and present a quantum algorithm to train this model. It is shown that the algorithm achieves an exponential speed-up over its classical counterpart. Furthermore, we also demonstrate that the QCRF model possesses higher Vapnik Chervonenkis dimension than the classical CRF model, which means QCRF is equipped with a higher learning ability.