Experimental demonstration of reconstructing quantum states with generative models

TL;DR

This paper demonstrates an experimental method using neural network generative models to efficiently reconstruct quantum states, significantly reducing resource requirements for larger systems.

Contribution

It presents the first experimental validation of quantum state reconstruction using machine learning, with linear scaling of samples for up to five qubits.

Findings

Successful reconstruction of GHZ and random states up to five qubits

Sample complexity scales linearly with system size

Machine learning offers a promising tool for quantum device validation

Abstract

Quantum state tomography, a process that reconstructs a quantum state from measurements on an ensemble of identically prepared copies, plays a crucial role in benchmarking quantum devices. However, brute-force approaches to quantum state tomography would become impractical for large systems, as the required resources scale exponentially with the system size. Here, we explore a machine learning approach and report an experimental demonstration of reconstructing quantum states based on neural network generative models with an array of programmable superconducting transmon qubits. In particular, we experimentally prepare the Greenberger-Horne-Zeilinger states and random states up to five qubits and demonstrate that the machine learning approach can efficiently reconstruct these states with the number of required experimental samples scaling linearly with system size. Our results experimentally showcase the intriguing potential for exploiting machine learning techniques in validating and characterizing complex quantum devices, offering a valuable guide for the future development of quantum technologies.