[HTML][HTML] The variational quantum eigensolver: a review of methods and best practices
The variational quantum eigensolver (or VQE), first developed by Peruzzo et al.(2014), has
received significant attention from the research community in recent years. It uses the …
received significant attention from the research community in recent years. It uses the …
Ab initio quantum chemistry with neural-network wavefunctions
Deep learning methods outperform human capabilities in pattern recognition and data
processing problems and now have an increasingly important role in scientific discovery. A …
processing problems and now have an increasingly important role in scientific discovery. A …
The OpenMolcas Web: A Community-Driven Approach to Advancing Computational Chemistry
G Li Manni, I Fdez. Galván, A Alavi… - Journal of chemical …, 2023 - ACS Publications
The developments of the open-source OpenMolcas chemistry software environment since
spring 2020 are described, with a focus on novel functionalities accessible in the stable …
spring 2020 are described, with a focus on novel functionalities accessible in the stable …
Many-body methods for surface chemistry come of age: Achieving consensus with experiments
The adsorption energy of a molecule onto the surface of a material underpins a wide array of
applications, spanning heterogeneous catalysis, gas storage, and many more. It is the key …
applications, spanning heterogeneous catalysis, gas storage, and many more. It is the key …
Discovering quantum phase transitions with fermionic neural networks
Deep neural networks have been very successful as highly accurate wave function Ansätze
for variational Monte Carlo calculations of molecular ground states. We present an extension …
for variational Monte Carlo calculations of molecular ground states. We present an extension …
A computational framework for neural network-based variational Monte Carlo with Forward Laplacian
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 …
cutting-edge technique of ab initio quantum chemistry. However, the high computational cost …
Quantum critical points and the sign problem
The “sign problem”(SP) is a fundamental limitation to simulations of strongly correlated
matter. It is often argued that the SP is not intrinsic to the physics of particular Hamiltonians …
matter. It is often argued that the SP is not intrinsic to the physics of particular Hamiltonians …
Interactions between large molecules pose a puzzle for reference quantum mechanical methods
Quantum-mechanical methods are used for understanding molecular interactions
throughout the natural sciences. Quantum diffusion Monte Carlo (DMC) and coupled cluster …
throughout the natural sciences. Quantum diffusion Monte Carlo (DMC) and coupled cluster …
Towards the ground state of molecules via diffusion Monte Carlo on neural networks
Abstract Diffusion Monte Carlo (DMC) based on fixed-node approximation has enjoyed
significant developments in the past decades and become one of the go-to methods when …
significant developments in the past decades and become one of the go-to methods when …
Neural wave functions for superfluids
Understanding superfluidity remains a major goal of condensed matter physics. Here, we
tackle this challenge utilizing the recently developed fermionic neural network (FermiNet) …
tackle this challenge utilizing the recently developed fermionic neural network (FermiNet) …