Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …

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 …

Artificial neural networks (ANNs) have been widely used for industrial applications and have
played a more important role in fundamental research. Although most ANN hardware …

Parametrized quantum circuits (PQCs) have been broadly used as a hybrid quantum-
classical machine learning scheme to accomplish generative tasks. However, whether …

Sampling from known probability distributions is a ubiquitous task in computational science,
underlying calculations in domains from linguistics to biology and physics. Generative …

A Markov chain update scheme using a machine-learned flow-based generative model is
proposed for Monte Carlo sampling in lattice field theories. The generative model may be …

We develop a machine learning method to construct accurate ground-state wave functions
of strongly interacting and entangled quantum spin as well as fermionic models on lattices. A …

In this paper, we demonstrate the expressibility of artificial neural networks (ANNs) in
quantum many-body physics by showing that a feed-forward neural network with a small …

In this Letter we supervisedly train neural networks to distinguish different topological
phases in the context of topological band insulators. After training with Hamiltonians of one …

Condensed matter physics (CMP) seeks to understand the microscopic interactions of matter
at the quantum and atomistic levels, and describes how these interactions result in both …