Correlation-Enhanced Neural Networks as Interpretable Variational Quantum States

TL;DR

This paper introduces a neural-network based variational ansatz for quantum states that captures relevant correlations, improving interpretability and effectiveness in modeling complex many-body systems.

Contribution

It presents a novel neural network parametrization that is both flexible and physically interpretable, suitable for complex quantum models.

Findings

Successfully applied to topological models

Effectively models long-range correlated systems

Enables exploration of low-lying excited states

Abstract

Variational methods have proven to be excellent tools to approximate ground states of complex many body Hamiltonians. Generic tools like neural networks are extremely powerful, but their parameters are not necessarily physically motivated. Thus, an efficient parametrization of the wave-function can become challenging. In this letter we introduce a neural-network based variational ansatz that retains the flexibility of these generic methods while allowing for a tunability with respect to the relevant correlations governing the physics of the system. We illustrate the success of this approach on topological, long-range correlated and frustrated models. Additionally, we introduce compatible variational optimization methods for exploration of low-lying excited states without symmetries that preserve the interpretability of the ansatz.