Local-measurement-based quantum state tomography via neural networks

TL;DR

This paper introduces a neural network-based method for quantum state tomography that reconstructs full quantum states from local measurements, demonstrating high accuracy on small systems and promising scalability for larger quantum systems.

Contribution

It presents a novel machine learning approach using neural networks to perform quantum state tomography from local measurements, enabling scalable reconstruction.

Findings

Achieves high accuracy in reconstructing 4-qubit states from local data.

Successfully applies the method to a 4-qubit NMR system dataset.

Paves the way for scalable quantum state tomography in larger systems.

Abstract

Quantum state tomography is a daunting challenge of experimental quantum computing even in moderate system size. One way to boost the efficiency of state tomography is via local measurements on reduced density matrices, but the reconstruction of the full state thereafter is hard. Here, we present a machine learning method to recover the full quantum state from its local information, where a fully-connected neural network is built to fulfill the task with up to seven qubits. In particular, we test the neural network model with a practical dataset, that in a 4-qubit nuclear magnetic resonance system our method yields global states via the 2-local information with high accuracy. Our work paves the way towards scalable state tomography in large quantum systems.