Search Results for author:
Found 5 papers, 1 papers with code
Information Scrambling in Quantum Neural Networks
In this Letter, we show that this process can also be viewed from the opposite direction: the quantum information in the output qubits is scrambled into the input.
Mutual Information Scaling and Expressive Power of Sequence Models
Sequence models assign probabilities to variable-length sequences such as natural language texts.
Deep Learning Topological Invariants of Band Insulators
Despite the complexity of the neural network, we find that the output of certain intermediate hidden layers resembles either the winding angle for models in AIII class or the solid angle (Berry curvature) for models in A class, indicating that neural networks essentially capture the mathematical formula of topological invariants.
Visualizing Neural Network Developing Perturbation Theory
In this letter, motivated by the question that whether the empirical fitting of data by neural network can yield the same structure of physical laws, we apply the neural network to a simple quantum mechanical two-body scattering problem with short-range potentials, which by itself also plays an important role in many branches of physics.
Machine Learning Topological Invariants with Neural Networks
In this Letter we supervisedly train neural networks to distinguish different topological phases in the context of topological band insulators.
