Deep Neural Networks for Quantum Monte Carlo Ansatzs

Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks

We introduce a novel deep learning architecture, the fermionic neural network, as a powerful wavefunction approximation for many-electron systems.We demonstrate that deep neural networks can improve the accuracy of variational quantum monte carlo to the point where it outperforms other ab-initio quantum chemistry methods, opening the possibility of accurate direct optimisation of wavefunctions for previously intractable molecules and solids.We predict the dissociation curves of the nitrogen molecule and hydrogen chain, two challenging strongly-correlated systems, to significantly higher accuracy than the coupled cluster method, widely considered the most accurate scalable method for quantum chemistry at equilibrium geometry.This demonstrates that deep neural networks can improve the accuracy of variational quantum monte carlo to the point where it outperforms other ab-initio quantum chemistry methods, opening the possibility of accurate direct optimisation of wavefunctions for previously intractable molecules and solids.