A sum-of-Gaussians approximation to the Fourier multiplier of the FFPE fundamental solution yields an O(M d N) separated representation that works in dimensions up to 10^5 with logarithmic growth in M for fixed accuracy.
arXiv preprint arXiv:2302.09019 , year =
12 Pith papers cite this work, alongside 14 external citations. Polarity classification is still indexing.
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A hybrid classical-quantum scheme compresses and disentangles bottleneck layers of pre-trained neural networks into MPO form for execution on quantum devices, validated via proof-of-concept on MNIST and CIFAR-10 image classification.
A functional tree tensor network framework derives Runge-Kutta order conditions by direct comparison of tensor-structured Taylor expansions for exact and numerical solutions.
T-GINEE combines CP tensor decomposition with a generalized estimating equation framework and task-specific loss to explicitly model inter-layer correlations in multilayer graphs while providing consistency and asymptotic normality guarantees under mild conditions.
NN-fTNS enhance fermionic tensor networks with neural parametrization to improve expressivity and achieve order-of-magnitude better energies than pure fTNS on Hubbard models while maintaining linear scaling.
A perceptrain-based variational ansatz achieves relative ground-state energy accuracy of 10^{-5} (VMC) to 10^{-6} (GFMC) on a 10x10 transverse-field Ising model with 1/r^6 interactions using ranks of only 2-5.
Hierarchical nonlinear tensor networks generate dense, conv, and attention weights from few cores, yielding extreme per-layer compression with competitive CIFAR-10 accuracy on VGG-16.
A two-layer tensor-augmented CNN reaches 93.7% test accuracy on Fashion-MNIST, matching or exceeding deeper models like VGG-16 and GoogLeNet by using tensors for richer expressivity.
A hybrid tensor network framework interpolates between classical and quantum models via controllable post-selection, with a trainable hyperparameter that complements bond dimension to enhance quantum machine learning.
A comprehensive review organizing progress at the AI-quantum information intersection from both directions.
Tensor networks developed for quantum states are reviewed as tools for machine learning models, with assessment of their potential computational, explanatory, and privacy advantages alongside remaining challenges.
A survey of variational quantum algorithms, quantum neural networks, and tensor networks for addressing scalability challenges in computational fluid dynamics.
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