We present large-scale quantum Monte Carlo simulation results on a realistic Hamiltonian of
kagome-lattice Rydberg atom arrays. Although the system has no intrinsic disorder …

Strongly interacting arrays of Rydberg atoms provide versatile platforms for exploring exotic
many-body phases and dynamics of correlated quantum systems. Motivated by recent …

Recurrent neural networks (RNNs), originally developed for natural language processing,
hold great promise for accurately describing strongly correlated quantum many-body …

Motivated by recent experimental realizations of exotic phases of matter on programmable
quantum simulators, we carry out a comprehensive theoretical study of quantum phase …

Large language models, like transformers, have recently demonstrated immense powers in
text and image generation. This success is driven by the ability to capture long-range …

Rydberg atom arrays are programmable quantum simulators capable of preparing
interacting qubit systems in a variety of quantum states. Due to long experimental …

For over a decade, machine learning (ML) models have been making strides in computer
vision and natural language processing (NLP), demonstrating high proficiency in …

Identifying phase transitions and classifying phases of matter is central to understanding the
properties and behavior of a broad range of material systems. In recent years, machine …

We perform a principal component analysis (PCA) of two one-dimensional lattice models
belonging to distinct nonequilibrium universality classes—directed bond percolation and …

One of the central tasks in many-body physics is the determination of phase diagrams.
However, map** out a phase diagram generally requires a great deal of human intuition …