用量子电路实现多语言翻译，准确率达82%
Multilingual Machine Translation with Quantum Encoder Decoder Attention-based Convolutional Variational Circuits
- 用量子卷积、量子注意力等构建新型量子编码解码架构
- 在OPUS数据集上实现英法德印四语翻译，准确率82%
- 为突破经典模型算力瓶颈提供量子计算新思路
基于云端的多语言翻译服务如Google Translate和Microsoft Translator已达到顶尖水平。这些系统依赖大规模多语言模型，如GRU、LSTM、BERT、GPT、T5等编码解码架构及注意力机制。此外，ChatGPT和DeepSeek等新一代自然语言系统在多项NLP任务中展现出巨大潜力，包括多语言翻译。然而，它们仍基于经典计算框架。QEDACVC（量子编码解码注意力卷积变分电路）提出一种替代方案，探索量子计算范式以实现多语言机器翻译。该模型引入量子编码解码架构，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模块，在量子计算硬件上模拟运行。在包含英语、法语、德语和印地语的OPUS数据集上训练后，其翻译准确率达到82%。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。