An emerging direction of quantum computing is to establish meaningful quantum
applications in various fields of artificial intelligence, including natural
language processing (NLP). Although some efforts based on syntactic analysis
have opened the door to research in Quantum NLP (QNLP), limitations such as
heavy syntactic preprocessing and syntax-dependent network architecture make
them impracticable on larger and real-world data sets. In this paper, we
propose a new simple network architecture, called the quantum self-attention
neural network (QSANN), which can make up for these limitations. Specifically,
we introduce the self-attention mechanism into quantum neural networks and then
utilize a Gaussian projected quantum self-attention serving as a sensible
quantum version of self-attention. As a result, QSANN is effective and scalable
on larger data sets and has the desirable property of being implementable on
near-term quantum devices. In particular, our QSANN outperforms the best
existing QNLP model based on syntactic analysis as well as a simple classical
self-attention neural network in numerical experiments of text classification
tasks on public data sets. We further show that our method exhibits robustness
to low-level quantum noises.

本文提出了一种名为 QSANN 的量子自我注意神经网络，它将自我注意机制引入到量子神经网络中，通过高斯投影解决了诸如大规模数据集和句法依赖性网络结构等限制；QSANN 在公共数据集的文本分类任务中表现优越，且具有鲁棒性和可扩展性。

文本分类的量子自注意神经网络

Quantum Self-Attention Neural Networks for Text Classification

Recent advancements in self-attention neural network architectures have
raised the bar for open-ended text generation. Yet, while current methods are
capable of producing a coherent text which is several hundred words long,
attaining control over the content that is being generated -- as well as
evaluating it -- are still open questions. We propose a controlled generation
task which is based on expanding a sequence of facts, expressed in natural
language, into a longer narrative. We introduce human-based evaluation metrics
for this task, as well as a method for deriving a large training dataset. We
evaluate three methods on this task, based on fine-tuning pre-trained models.
We show that while auto-regressive, unidirectional Language Models such as GPT2
produce better fluency, they struggle to adhere to the requested facts. We
propose a plan-and-cloze model (using fine-tuned XLNet) which produces
competitive fluency while adhering to the requested content.

该研究提出了一个基于自然语言的控制生成任务，可将一系列事实扩展为更长的叙述，并通过引入人类评估指标和大型训练数据集的方法评估了三种方法，证明了自回归的单向语言模型如 GPT2 的生成流畅度更好，但很难遵循所请求的事实，提出了一个基于计划和填空模型的解决方案（使用精细调整的 XLNet），其生成流畅度有竞争力，同时遵循所请求的内容。