Deep learning methods outperform human capabilities in pattern recognition and data
processing problems and now have an increasingly important role in scientific discovery. A …

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 …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Neural network-based variational Monte Carlo (NN-VMC) has emerged as a promising
cutting-edge technique of ab initio quantum chemistry. However, the high computational cost …

Inspired by the advancements in large language models based on transformers, we
introduce the transformer quantum state (TQS): a versatile machine learning model for …

Variational wave function ansätze are at the heart of solving quantum many-body
problems in physics and chemistry. Previous designs of hardware-efficient ansatz (HEA) on …

Numerically simulating large, spinful, fermionic systems is of great interest in condensed
matter physics. However, the exponential growth of the Hilbert space dimension with system …

The stochastic reaction network in which chemical species evolve through a set of reactions
is widely used to model stochastic processes in physics, chemistry and biology. To …

Variational optimization of neural-network representations of quantum states has been
successfully applied to solve interacting fermionic problems. Despite rapid developments …

The configuration interaction approach provides a conceptually simple and powerful
approach to solve the Schrödinger equation for realistic molecules and materials but is …