This is a collection of files containing the matlab code for generating the dataset, the dataset and mxnet code for training and testing the machine learning models as well as python code for plotting the experimental results related to the work "Entanglement Structure Detection via Machine Learning" to appear in the journal of "Quantum Science and Technology" by Changbo Chen, Changliang Ren, Hongqing Lin and He Lu. For any questions related to these data and code, please contact Changbo Chen (firstname.lastname@hotmail.com)
We demonstrate how machine learning is able to model experiments in quantum physics. Quantum entangl...
This is supplemental data and code for: D. Hothem et al., Learning a quantum computer's capability u...
This repository contains the codes used for the article "Proofs on network quantum nonlocality aided...
input data to reproduce the calculations performed as in the repositoty: https://github.com/janweinr...
Machine learning techniques have been successfully applied to classifying an extensive range of phen...
© 2018 American Physical Society. The problem of determining whether a given quantum state is entang...
We introduce a machine learning model, the q-CNN model, sharing key features with convolutional neur...
We describe a multi-disciplinary project to use machine learning techniques based on neural networks...
202209 bchyVersion of RecordRGCOthersNational Natural Science Foundation of China; Shenzhen Fundamen...
This repository provides the source code for some analytical and numerical implementations of the Ph...
As an important resource to realize quantum information, quantum correlation displays different beha...
Several proposals have been recently introduced to implement Quantum Machine Learning (QML) algorith...
Entanglement constitutes a key characteristic feature of quantum matter. Its detection, however, sti...
The use of computational algorithms, implemented on a computer, to extract information from data has...
Raw data for the manuscript "Provably efficient machine learning for quantum many-body problems"
We demonstrate how machine learning is able to model experiments in quantum physics. Quantum entangl...
This is supplemental data and code for: D. Hothem et al., Learning a quantum computer's capability u...
This repository contains the codes used for the article "Proofs on network quantum nonlocality aided...
input data to reproduce the calculations performed as in the repositoty: https://github.com/janweinr...
Machine learning techniques have been successfully applied to classifying an extensive range of phen...
© 2018 American Physical Society. The problem of determining whether a given quantum state is entang...
We introduce a machine learning model, the q-CNN model, sharing key features with convolutional neur...
We describe a multi-disciplinary project to use machine learning techniques based on neural networks...
202209 bchyVersion of RecordRGCOthersNational Natural Science Foundation of China; Shenzhen Fundamen...
This repository provides the source code for some analytical and numerical implementations of the Ph...
As an important resource to realize quantum information, quantum correlation displays different beha...
Several proposals have been recently introduced to implement Quantum Machine Learning (QML) algorith...
Entanglement constitutes a key characteristic feature of quantum matter. Its detection, however, sti...
The use of computational algorithms, implemented on a computer, to extract information from data has...
Raw data for the manuscript "Provably efficient machine learning for quantum many-body problems"
We demonstrate how machine learning is able to model experiments in quantum physics. Quantum entangl...
This is supplemental data and code for: D. Hothem et al., Learning a quantum computer's capability u...
This repository contains the codes used for the article "Proofs on network quantum nonlocality aided...