Quantum-Inspired Transformers are a new class of approaches for natural language processing (NLP) that use principles inspired by physical quantum mechanics to encode and process linguistic information. Paper aims to improve both scalability and efficiency for large-scale NLP tasks in the next generation of artificial intelligence (AI). However, traditional transformers can struggle to encode complex semantic relationships across long sequences and can require high levels of compute resources, hence limiting scalability for large datasets and multilingual tasks. In addition, traditional embeddings do not represent multiple contextual meanings simultaneously, which decreases model expressiveness. To address these challenges and maximize the exploration of potential quantum effects in AI, this paper presents a Quantum State Embedding Attention (QSEA) framework that encodes tokens as quantum inspired states with complex-valued amplitudes. Attention, which is based on entangled states, is meant to efficiently capture long-range dependencies while reducing compute and increasing scalability, all without losing a rich understanding of context. Whereas multilingual translation needs to encode both the source similarity and the target language within a smaller semantic representation space. Our experimentation shows that QSEA consistently provides increased accuracy in decoding, measures attention across long sequences, and yields enduring scalability over large multilingual datasets. The evidence demonstrates the potential for QSEA to be the new layer in the future of AI systems, a layer that is scalable and a highperforming NLP application.
Vij et al. (Thu,) studied this question.
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