A framework for efficient ab initio electronic structure with Gaussian Process States

Understand

We present a general framework for the efficient simulation of realistic fermionic systems with modern machine learning inspired representations of quantum many-body states, towards a universal tool for ab initio electronic structure.

• These machine learning inspired ansatzes have recently come to the fore in both a (first quantized) continuum and discrete Fock space representations, where however the inherent scaling of the latter approach for realistic interactions has so far limited practical applications.
• With application to the 'Gaussian Process State', a recently introduced ansatz inspired by systematically improvable kernel models in machine learning, we discuss different choices to define the representation of the computational Fock space.
• We show how local representations are particularly suited for stochastic sampling of expectation values, while also indicating a route to overcome the discrepancy in the scaling compared to continuum formulated models.