Quantum states from normalizing flows

TL;DR

This paper presents a novel neural network architecture based on normalizing flows for representing quantum states, enabling efficient sampling and accurate simulation of many-body quantum systems.

Contribution

It introduces a new neural quantum state architecture using normalizing flows, improving sampling efficiency and providing systematic error estimation methods.

Findings

Efficient uncorrelated sampling of quantum states.

Successful ground-state and real-time evolution simulations.

Method for rigorous error estimation in neural quantum states.

Abstract

We introduce an architecture for neural quantum states for many-body quantum-mechanical systems, based on normalizing flows. The use of normalizing flows enables efficient uncorrelated sampling of configurations from the probability distribution defined by the wavefunction, mitigating a major cost of using neural states in simulation. We demonstrate the use of this architecture for both ground-state preparation (for self-interacting particles in a harmonic trap) and real-time evolution (for one-dimensional tunneling). Finally, we detail a procedure for obtaining rigorous estimates of the systematic error when using neural states to approximate quantum evolution.