Multilingual Machine Translation with Quantum Encoder Decoder Attention-based Convolutional Variational Circuits

TL;DR

This paper introduces QEDACVC, a quantum computing-based encoder-decoder architecture for multilingual machine translation, demonstrating promising accuracy on the OPUS dataset across multiple languages.

Contribution

It presents a novel quantum encoder-decoder model for multilingual translation, replacing classical components with quantum analogs, a pioneering approach in the field.

Findings

Achieved 82% accuracy on OPUS multilingual dataset

Successfully implemented quantum convolution and attention mechanisms

Demonstrated potential of quantum models in NLP tasks

Abstract

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.