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Tensor network compressibility of convolutional models
We explore this by assessing how truncating the convolution kernels of dense (untensorized) CNNs impact their accuracy.
CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networks
Large Language Models (LLMs) such as ChatGPT and LlaMA are advancing rapidly in generative Artificial Intelligence (AI), but their immense size poses significant challenges, such as huge training and inference costs, substantial energy demands, and limitations for on-site deployment.
Efficient tensor network simulation of IBM's largest quantum processors
Apart from simulating the original experiment for 127 qubits, we also extend our results to 433 and 1121 qubits, and for evolution times around 8 times longer, thus setting a benchmark for the newest IBM quantum machines.
Quantum-Inspired Tensor Neural Networks for Option Pricing
Recent advances in deep learning have enabled us to address the curse of dimensionality (COD) by solving problems in higher dimensions.
Quantum-Inspired Tensor Neural Networks for Partial Differential Equations
We demonstrate that TNN provide significant parameter savings while attaining the same accuracy as compared to the classical Dense Neural Network (DNN).
