Guided by grammatical structure, words compose to form sentences, and guided by discourse structure, sentences compose to form dialogues and documents. The compositional aspect of sentence and discourse units is often overlooked by machine learning algorithms. A recent initiative called Quantum Natural Language Processing (QNLP) learns word meanings as points in a Hilbert space and acts on them via a translation of grammatical structure into Parametrised Quantum Circuits (PQCs). Previous work extended the QNLP translation to discourse structure using points in a closure of Hilbert spaces. In this paper, we evaluate this translation on a Winograd-style pronoun resolution task. We train a Variational Quantum Classifier (VQC) for binary classification and implement an end-to-end pronoun resolution system. The simulations executed on IBMQ software converged with an F1 score of 87.20%. The model outperformed two out of three classical coreference resolution systems and neared state-of-the-art SpanBERT. A mixed quantum-classical model yet improved these results with an F1 score increase of around 6%.

通过在希尔伯特空间中学习单词的意义并通过将语法结构转化为参数化量子电路的翻译，我们评估了Quantum Natural Language Processing (QNLP) 对Winograd风格的代词消解任务的翻译，并训练了一个用于二元分类的Variational Quantum Classifier (VQC)，在IBMQ软件上执行的模拟获得了87.20%的F1分数，该模型优于三个经典共指消解系统中的两个，并接近最先进的SpanBERT，而一个混合量子-经典模型则将这些结果进一步提升了约6%。

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