A key challenge in the effort to simulate today's quantum computing devices is the ability to
learn and encode the complex correlations that occur between qubits. Emerging …

The continued development of computational approaches to many-body ground-state
problems in physics and chemistry calls for a consistent way to assess its overall progress …

Deep neural networks have become a highly accurate and powerful wavefunction ansatz in
combination with variational Monte Carlo methods for solving the electronic Schrödinger …

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 …

Obtaining accurate solutions to the Schrödinger equation is the key challenge in
computational quantum chemistry. Deep-learning-based Variational Monte Carlo (DL-VMC) …

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 …

Recently, tremendous progress has been made in the field of quantum science and
technologies: different platforms for quantum simulation as well as quantum computing …

As quantum technology rapidly advances, the need for efficient scalable methods to
characterize quantum systems intensifies. Quantum state tomography and Hamiltonian …

Abstract Machine learning has shown significant breakthroughs in quantum science, where
in particular deep neural networks exhibited remarkable power in modeling quantum many …

Neural-network quantum states (NQSs), variationally optimized by combining traditional
methods and deep learning techniques, is a new way to find quantum many-body ground …