# Experimental quantum homodyne tomography via machine learning

## TL;DR

This paper introduces an experimental method using neural networks, specifically restricted Boltzmann machines, for efficient and complete quantum state characterization via homodyne tomography, reducing data requirements and overfitting.

## Contribution

The paper presents a novel neural network-based approach for quantum state tomography that is more efficient and scalable than existing methods.

## Key findings

- Allows full quantum state estimation with less data
- Reduces overfitting in quantum state reconstruction
- Applicable to various large-scale quantum systems

## Abstract

Complete characterization of states and processes that occur within quantum devices is crucial for understanding and testing their potential to outperform classical technologies for communications and computing. However, solving this task with current state-of-the-art techniques becomes unwieldy for large and complex quantum systems. Here we realize and experimentally demonstrate a method for complete characterization of a quantum harmonic oscillator based on an artificial neural network known as the restricted Boltzmann machine. We apply the method to optical homodyne tomography and show it to allow full estimation of quantum states based on a smaller amount of experimental data compared to state-of-the-art methods. We link this advantage to reduced overfitting. Although our experiment is in the optical domain, our method provides a way of exploring quantum resources in a broad class of large-scale physical systems, such as superconducting circuits, atomic and molecular ensembles, and optomechanical systems.