• K. Sahithi
• Durgunala Ranjith
• A. Vaishnavi
• CH. Shashidhar

The growing demand for multilingual communication has exposed key challenges in Natural Language Processing (NLP), as existing models struggle with long-term dependencies and cross-lingual tasks, especially in low-resource languages. To address this, the Quantum-Assisted LSTM Transformer for Cross-Lingual NLP introduces a hybrid framework that combines the sequential memory of LSTMs, the contextual learning of Transformers, and the enhanced feature representation of quantum-inspired methods. By integrating quantum-assisted embeddings with deep learning, the model more effectively captures relationships across languages, enabling improved performance in machine translation, cross-lingual sentiment analysis, and multilingual text understanding, while offering a scalable and innovative solution to modern NLP challenges.

Quantum-Assisted Embeddings, LSTM (Long Short-Term Memory), Transformer Architecture, Machine Translation, Deep Learning, Natural Language Processing.

"Quantum-Assisted LSTM Transformer for Cross-Lingual NLP", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 3, page no.h583-h594, March-2026, Available :http://www.jetir.org/papers/JETIR2603775.pdf

"Quantum-Assisted LSTM Transformer for Cross-Lingual NLP", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 3, page no. pph583-h594, March-2026, Available at : http://www.jetir.org/papers/JETIR2603775.pdf