Phase Transition Study Meets Machine Learning

Abstract

In recent years, machine learning (ML) techniques have emerged as powerful tools for studying many-body complex systems, and encompassing phase transitions in various domains of physics. This mini review provides a concise yet comprehensive examination of the advancements achieved in applying ML to investigate phase transitions, with a primary focus on those involved in nuclear matter studies.

Machine-learning-assisted efficient reconstruction of the quantum states generated from the Sagnac polarization-entangled photon source

“…On the other hand, machine learning (ML) is a branch of artificial intelligence (AI) that focuses on the development of algorithms and models capable of learning from data to make predictions or decisions. Over the past decades, machine learning has increasingly been used in various fields of physics for solving diverse tasks, [13,14] such as particle physics, [15] astronomy, [16] material sciences. [17] In recent years, machine learning has been demonstrated as a novel and efficient platform for solving problems in quantum information science, [18] such as classification of quantum entangled states, [19][20][21] es-timation of quantum states, [22][23][24] and development of novel algorithms for solving many-body systems.…”