Enhancing quantum state tomography via resource-efficient attention-based neural networks

TL;DR

This paper introduces an attention-based neural network protocol that enhances quantum state tomography, significantly reducing training data needs and improving fidelity, with applications in quantum metrology and current quantum platforms.

Contribution

It presents a novel hybrid tomography protocol combining traditional methods with neural networks, improving efficiency and fidelity in finite-data regimes.

Findings

Improved average fidelity over traditional methods.

Reduces training data requirements by an order of magnitude.

Applicable to current quantum simulation platforms.

Abstract

Resource-efficient quantum state tomography is one of the key ingredients of future quantum technologies. In this work, we propose a new tomography protocol combining standard quantum state reconstruction methods with an attention-based neural network architecture. We show how the proposed protocol is able to improve the averaged fidelity reconstruction over linear inversion and maximum-likelihood estimation in the finite-statistics regime, reducing at least by an order of magnitude the amount of necessary training data. We demonstrate the potential use of our protocol in physically relevant scenarios, in particular, to certify metrological resources in the form of many-body entanglement generated during the spin squeezing protocols. This could be implemented with the current quantum simulator platforms, such as trapped ions, and ultra-cold atoms in optical lattices.