Bidirectional information flow quantum state tomography

TL;DR

This paper introduces a novel quantum state tomography method using Bidirectional Gated Recurrent Units (BiGRU) neural networks, enabling efficient reconstruction of complex quantum states with fewer measurements and high fidelity.

Contribution

It presents a new BiGRU-based neural network approach for quantum state tomography, improving efficiency and accuracy over existing methods.

Findings

Fewer measurement samples needed for high-fidelity reconstruction

Effective reconstruction of both easy and hard quantum states

Neural network approach enhances quantum state tomography

Abstract

The exact reconstruction of many-body quantum systems is one of the major challenges in modern physics, because it is impractical to overcome the exponential complexity problem brought by high-dimensional quantum many-body systems. Recently, machine learning techniques are well used to promote quantum information research and quantum state tomography has been also developed by neural network generative models. We propose a quantum state tomography method, which is based on Bidirectional Gated Recurrent Unit neural network (BiGRU), to learn and reconstruct both easy quantum states and hard quantum states in this paper. We are able to use fewer measurement samples in our method to reconstruct these quantum states and obtain high fidelity.