Search Results for author:
Found 6 papers, 0 papers with code
Active Learning Approach to Optimization of Experimental Control
In this work we present a general machine learning based scheme to optimize experimental control.
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.
Emergent Quantum Mechanics in an Introspective Machine Learning Architecture
Can physical concepts and laws emerge in a neural network as it learns to predict the observation data of physical systems?
Disordered Systems and Neural Networks Quantum Physics
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.
