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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
Traditional compression methods such as pruning, distillation, and low-rank approximation focus on reducing the effective number of neurons in the network, while quantization focuses on reducing the numerical precision of individual weights to reduce the model size while keeping the number of neurons fixed.
Boosting Defect Detection in Manufacturing using Tensor Convolutional Neural Networks
Defect detection is one of the most important yet challenging tasks in the quality control stage in the manufacturing sector.
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.
A programming guide for tensor networks with global $SU(2)$ symmetry
This paper is a manual with tips and tricks for programming tensor network algorithms with global $SU(2)$ symmetry.
Strongly Correlated Electrons Quantum Physics
