Experimental Machine Learning of Quantum States

TL;DR

This paper demonstrates an experimental machine learning approach to classify quantum states efficiently, reducing resource consumption compared to traditional quantum state tomography, and enhancing classification performance with neural network improvements.

Contribution

It introduces a machine learning method to classify quantum states experimentally, avoiding full state tomography and improving accuracy with neural network architecture modifications.

Findings

Neural networks can classify quantum states without full state information.

Adding hidden layers improves classification accuracy.

The approach reduces resource requirements for quantum state analysis.

Abstract

Quantum information technologies provide promising applications in communication and computation, while machine learning has become a powerful technique for extracting meaningful structures in 'big data'. A crossover between quantum information and machine learning represents a new interdisciplinary area stimulating progresses in both fields. Traditionally, a quantum state is characterized by quantum state tomography, which is a resource-consuming process when scaled up. Here we experimentally demonstrate a machine-learning approach to construct a quantum-state classifier for identifying the separability of quantum states. We show that it is possible to experimentally train an artificial neural network to efficiently learn and classify quantum states, without the need of obtaining the full information of the states. We also show how adding a hidden layer of neurons to the neural network can significantly boost the performance of the state classifier. These results shed new light on how classification of quantum states can be achieved with limited resources, and represent a step towards machine-learning-based applications in quantum information processing.