In recent years, machine learning has emerged as a powerful computational tool and novel
problem-solving perspective for physics, offering new avenues for studying strongly …

Strongly correlated systems have been challenging to computational chemists for a long
time. To solve these systems, multireference methods have been developed over the years …

Phase transition is one of the most important phenomena in nature and plays a central role
in materials design. All phase transitions are characterized by suitable order parameters …

Due to the exponential growth of the Hilbert space dimension with system size, the
simulation of quantum many-body systems has remained a persistent challenge until today …

Coherent Ising Machine (CIM) is a network of optical parametric oscillators that solve
combinatorial optimization problems by finding the ground state of an Ising Hamiltonian. In …

We develop a self-supervised ensemble learning (SSEL) method to accurately classify
distinct types of phase transitions by analyzing the fluctuation properties of machine learning …

In this article, we explore the potential of artificial neural networks, which are trained using
an exceptionally simplified catalog of ideal configurations encompassing both order and …

This paper presents the investigation of convolutional neural network (CNN) prediction
successfully recognizing the temperature of the nonequilibrium phase transitions in two …

The detection of phase transitions is a fundamental challenge in condensed matter physics,
traditionally addressed through analytical methods and direct numerical simulations. In …

Numerical generation of physical states is essential to all scientific research fields. The role
of a numerical generator is not limited to understanding experimental results; it can also be …