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Found 10 papers, 6 papers with code
Quantum Hamiltonian-Based Models and the Variational Quantum Thermalizer Algorithm
We introduce a new class of generative quantum-neural-network-based models called Quantum Hamiltonian-Based Models (QHBMs).
Quantum Graph Neural Networks
We introduce Quantum Graph Neural Networks (QGNN), a new class of quantum neural network ansatze which are tailored to represent quantum processes which have a graph structure, and are particularly suitable to be executed on distributed quantum systems over a quantum network.
TensorNetwork on TensorFlow: Entanglement Renormalization for quantum critical lattice models
We use the MERA to approximate the ground state wave function of the infinite, one-dimensional transverse field Ising model at criticality, and extract conformal data from the optimized ansatz.
Computational Physics
TensorNetwork for Machine Learning
We demonstrate the use of tensor networks for image classification with the TensorNetwork open source library.
TensorNetwork: A Library for Physics and Machine Learning
TensorNetwork is an open source library for implementing tensor network algorithms.
TensorNetwork on TensorFlow: A Spin Chain Application Using Tree Tensor Networks
TensorNetwork is an open source library for implementing tensor network algorithms in TensorFlow.
Theory III: Dynamics and Generalization in Deep Networks
In particular, gradient descent induces a dynamics of the normalized weights which converge for $t \to \infty$ to an equilibrium which corresponds to a minimum norm (or maximum margin) solution.
A Surprising Linear Relationship Predicts Test Performance in Deep Networks
Given two networks with the same training loss on a dataset, when would they have drastically different test losses and errors?
Theory IIIb: Generalization in Deep Networks
Here we prove a similar result for nonlinear multilayer DNNs near zero minima of the empirical loss.
Theory of Deep Learning III: explaining the non-overfitting puzzle
In this note, we show that the dynamics associated to gradient descent minimization of nonlinear networks is topologically equivalent, near the asymptotically stable minima of the empirical error, to linear gradient system in a quadratic potential with a degenerate (for square loss) or almost degenerate (for logistic or crossentropy loss) Hessian.
