Neural network state estimation for full quantum state tomography

TL;DR

This paper introduces a neural network-based method for full quantum state tomography that is highly efficient, accurate, and scalable, outperforming existing algorithms in computational complexity.

Contribution

The paper presents a novel neural network estimation model for quantum state tomography that improves efficiency and scalability over previous methods.

Findings

The neural network model achieves high accuracy in state estimation.

The method demonstrates superior computational efficiency.

Numerical tests confirm scalability and effectiveness.

Abstract

An efficient state estimation model, neural network estimation (NNE), empowered by machine learning techniques, is presented for full quantum state tomography (FQST). A parameterized function based on neural network is applied to map the measurement outcomes to the estimated quantum states. Parameters are updated with supervised learning procedures. From the computational complexity perspective our algorithm is the most efficient one among existing state estimation algorithms for full quantum state tomography. We perform numerical tests to prove both the accuracy and scalability of our model.