The present study aims to explore the feasibility of language translation
using quantum natural language processing algorithms on noisy
intermediate-scale quantum (NISQ) devices. Classical methods in natural
language processing (NLP) struggle with handling large-scale computations
required for complex language tasks, but quantum NLP on NISQ devices holds
promise in harnessing quantum parallelism and entanglement to efficiently
process and analyze vast amounts of linguistic data, potentially
revolutionizing NLP applications. Our research endeavors to pave the way for
quantum neural machine translation, which could potentially offer advantages
over classical methods in the future. We employ Shannon entropy to demonstrate
the significant role of some appropriate angles of rotation gates in the
performance of parametrized quantum circuits. In particular, we utilize these
angles (parameters) as a means of communication between quantum circuits of
different languages. To achieve our objective, we adopt the encoder-decoder
model of classical neural networks and implement the translation task using
long short-term memory (LSTM). Our experiments involved 160 samples comprising
English sentences and their Persian translations. We trained the models with
different optimisers implementing stochastic gradient descent (SGD) as primary
and subsequently incorporating two additional optimizers in conjunction with
SGD. Notably, we achieved optimal results-with mean absolute error of 0.03,
mean squared error of 0.002, and 0.016 loss-by training the best model,
consisting of two LSTM layers and using the Adam optimiser. Our small dataset,
though consisting of simple synonymous sentences with word-to-word mappings,
points to the utility of Shannon entropy as a figure of merit in more complex
machine translation models for intricate sentence structures.

本研究在噪声中间规模量子（NISQ）设备上探索了使用量子自然语言处理算法进行语言翻译的可行性。我们采用香农熵来展示旋转门的适当角度在参数化量子电路性能中的重要作用，并利用这些角度（参数）作为不同语言的量子电路之间的通信方式，最终通过经典神经网络的编码器 - 解码器模型和长短期记忆（LSTM）的翻译任务实现了量子神经机器翻译，该翻译方法相对于经典方法具有潜在优势。在 160 个样本的实验中，我们使用了三种优化器训练模型，并通过使用 Adam 优化器训练具有两个 LSTM 层的最佳模型获得了最佳结果，其平均绝对误差为 0.03，均方误差为 0.002，损失为 0.016。我们的研究结果表明，尽管使用简单的同义句进行训练，但 Shannon 熵在处理复杂句子结构的更复杂机器翻译模型中具有一定的实用性。