It is a critical challenge to simultaneously achieve high interpretability and high efficiency
with the current schemes of deep machine learning (ML). The tensor network (TN), a well …

The variational quantum eigensolver (VQE) is regarded as a promising candidate of hybrid
quantum-classical algorithms for near-term quantum computers. Meanwhile, VQE is …

Variational quantum eigensolver (VQE) is regarded as a promising candidate of hybrid
quantum-classical algorithm for the near-term quantum computers. Meanwhile, VQE is …

The rapid development of quantum computing technologies already made it possible to
manipulate a collective state of several dozens of qubits, which poses a strong demand on …

Abstract Machine learning (ML) is a promising tool for the detection of phases of matter.
However, ML models are also known for their black-box construction, which hinders …

Catastrophic events have been commonly referred to as phase transitions in complex
systems (CS). This paper proposes an approach based on unsupervised machine learning …

Machine learning for phase transition has received intensive research interest in recent
years. However, its application in percolation still remains challenging. We propose an …

t-stochastic neighbor embedding (t-SNE) is a nonparametric data visualization method in
classical machine learning. It maps the data from the high-dimensional space into a low …

Unsupervised machine learning applied to the study of phase transitions is an ongoing and
interesting research direction. The active contour model, also called the snake model, was …

Machine learning approach to study quantum phase transitions of a frustrated one dimensional
spin-1/2 system - IOPscience This site uses cookies. By continuing to use this site you agree to …