QIMNLP: A Lightweight Quantum-Inspired Multilingual Natural Language Processing Model

We present QIMNLP (Quantum-Interference Modulation  Natural Language Processing), a novel byte-level language model that incorporates quantum interference modulation for enhanced multilingual text generation. Our approach introduces a Quantum Interference Modulator that applies position-dependent modulation to token embeddings, enabling more nuanced representation learning across multiple languages. The model achieves competitive performance on multilingual tasks while maintaining computational efficiency suitable for edge deployment. With only 43,085 parameters (~0.16 MB), QIMNLP demonstrates effective language modeling, language identification, and word boundary detection across three African languages: Luo, Kikuyu, and Lubukusu.