Quantum Large Language Models via Tensor Network Disentanglers

Abstract:We introduce a framework for seamlessly integrating quantum computing into pretrained large language models (LLMs). The key idea is to construct a hybrid quantum-classical representation that exactly reproduces the original model, providing a principled starting point from which quantum resources can only improve performance. Our approach replaces the weight matrices in self-attention and multilayer perceptron layers with two variational quantum circuits coupled to a matrix product operator (MPO). Tensor network disentanglers transfer much of each layer's information into the quantum circuits, enabling the remaining tensor network to be compressed to a bond-dimension-one MPO with over three orders of magnitude fewer classical parameters (in our experiments, from 110,592 to approximately 36 for the replaced layer) and less than a 0.3\% increase in perplexity. Training an added unitary adapter on top of this representation then surpasses the original model, reducing perplexity by up to 1.6\%. Finally, we validate the hybrid architecture on a real quantum processor, demonstrating a practical route towards quantum-enhanced language models.

Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2410.17397 [quant-ph]

(or arXiv:2410.17397v2 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2410.17397 Focus to learn more arXiv-issued DOI via DataCite

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