采用基于量子编码器-解码器注意力的卷积变分电路的多语言机器翻译
Multilingual Machine Translation with Quantum Encoder Decoder Attention-based Convolutional Variational Circuits
AI总结：
本文提出 QEDACVC，用量子卷积、池化、变分电路和注意力构建编码器-解码器，在 OPUS 四语种语料上实现 82% 准确率。
摘要：
Google Translate 和 Microsoft Translator 等基于云的多语言翻译服务实现了最先进的翻译能力。这些服务本质上使用 GRU、LSTM、BERT、GPT、T5 等大型多语言语言模型，或带有注意力机制的类似编码器-解码器架构作为骨干。此外，ChatGPT 和 DeepSeek 等新时代自然语言系统在自然语言处理的多项任务中展现出巨大潜力，同时也具备出色的多语言翻译能力。然而，这些模型以经典计算领域作为后端。QEDACVC（Quantum Encoder Decoder Attention-based Convolutional Variational Circuits，基于量子编码器-解码器注意力的卷积变分电路）是一种替代方案，它探索量子计算领域而非经典计算领域，以研究和演示多语言机器翻译。QEDACVC 引入量子编码器-解码器架构，通过量子卷积、量子池化、量子变分电路和量子注意力作为软件改造，在量子计算硬件上进行模拟和运行。QEDACVC 在 OPUS 数据集上针对英语、法语、德语和印地语语料进行多语言翻译训练时，取得了 82% 的准确率。
英文摘要：
Cloud-based multilingual translation services like Google Translate and Microsoft Translator achieve state-of-the-art translation capabilities. These services inherently use large multilingual language models such as GRU, LSTM, BERT, GPT, T5, or similar encoder-decoder architectures with attention mechanisms as the backbone. Also, new age natural language systems, for instance ChatGPT and DeepSeek, have established huge potential in multiple tasks in natural language processing. At the same time, they also possess outstanding multilingual translation capabilities. However, these models use the classical computing realm as a backend. QEDACVC (Quantum Encoder Decoder Attention-based Convolutional Variational Circuits) is an alternate solution that explores the quantum computing realm instead of the classical computing realm to study and demonstrate multilingual machine translation. QEDACVC introduces the quantum encoder-decoder architecture that simulates and runs on quantum computing hardware via quantum convolution, quantum pooling, quantum variational circuit, and quantum attention as software alterations. QEDACVC achieves an Accuracy of 82% when trained on the OPUS dataset for English, French, German, and Hindi corpora for multilingual translations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
