Quantum computers are expected to surpass the computational capabilities of classical
computers during this decade and have transformative impact on numerous industry sectors …

The purpose of this paper is to explore the applications of quantum computing to energy
systems optimization problems and discuss some of the challenges faced by quantum …

Supervised learning on molecules has incredible potential to be useful in chemistry, drug
discovery, and materials science. Luckily, several promising and closely related neural …

We introduce a scheme for molecular simulations, the deep potential molecular dynamics
(DPMD) method, based on a many-body potential and interatomic forces generated by a …

The challenge posed by the many-body problem in quantum physics originates from the
difficulty of describing the nontrivial correlations encoded in the exponential complexity of …

Classical machine learning (ML) provides a potentially powerful approach to solving
challenging quantum many-body problems in physics and chemistry. However, the …

A combined quantum mechanical (QM) and molecular mechanical (MM) potential has been
developed for the study of reactions in condensed phases. For the quantum mechanical …

Computing the ground state of interacting quantum matter is a long-standing challenge,
especially for complex two-dimensional systems. Recent developments have highlighted the …

Simulated annealing is a stochastic strategy for searching the ground state. A fast simulated
annealing (FSA) is a semi-local search and consists of occasional long jumps. The cooling …

In experimentally realistic situations, quantum systems are never perfectly isolated and the
coupling to their environment needs to be taken into account. Often, the effect of the …