Towards provably efficient quantum algorithms for large-scale machine learning models

Data for "Towards provably efficient quantum algorithms for large-scale machine-learning models". Error.txt includes the error proxy estimated due to Carleman linearization. The estimates are obtained using Hessian eigenvalues. Hessians.zip contains hessian eigenvalue grids and densities. Most files are for the 7 M parameter mode, and resnet_422-4-* are for the 103 M parameter model. Accuracy.txt contains the sparse training model accuracy on classifying the test set with CIFAR-100, as well as the loss values. Hessian_vrification.ipynb contains the code to generate the supplementary verification of Hessian eigenvalues on the error properties of Carleman linearization plots. The initial conditions are random.