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Quantum Large Language Models via Tensor Network Disentanglers

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arxiv 2410.17397 v1 pith:3EAXOSH5 submitted 2024-10-22 quant-ph cs.AIcs.LG

Borja Aizpurua , Saeed S. Jahromi , Sukhbinder Singh , Roman Orus This is my paper · ORCID
classification quant-phcs.AIcs.LG
keywords networkquantumtensormodelscircuitsclassicaldisentanglerslanguage
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We propose a method to enhance the performance of Large Language Models (LLMs) by integrating quantum computing and quantum-inspired techniques. Specifically, our approach involves replacing the weight matrices in the Self-Attention and Multi-layer Perceptron layers with a combination of two variational quantum circuits and a quantum-inspired tensor network, such as a Matrix Product Operator (MPO). This substitution enables the reproduction of classical LLM functionality by decomposing weight matrices through the application of tensor network disentanglers and MPOs, leveraging well-established tensor network techniques. By incorporating more complex and deeper quantum circuits, along with increasing the bond dimensions of the MPOs, our method captures additional correlations within the quantum-enhanced LLM, leading to improved accuracy beyond classical models while maintaining low memory overhead.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Numerical Optimization for Tensor Disentanglement

    math.NA 2025-08 conditional novelty 5.0 of 10

    Riemannian optimization, alternating truncated-SVD/Procrustes steps, and binary-search rank selection are presented as a framework for finding orthogonal disentangler rotations that reduce tensor-network bond dimensions.

  2. Quantum Neural Networks for Cloud Cover Parameterizations in Climate Models

    quant-ph 2025-02 conditional novelty 5.0 of 10

    Quantum neural networks predict cloud cover as accurately as similarly sized classical neural networks on coarse-grained storm-resolving climate data, while both outperform a fitted Xu-Randall baseline.

  3. Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks

    cs.LG 2025-05 conditional novelty 4.0 of 10

    The paper makes the case that tensorized neural networks offer valuable compression, scaling, and interpretability advantages that the deep learning community has not yet fully exploited.

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