All-optical neural network quantum state tomography

TL;DR

This paper presents an all-optical neural network setup for quantum state tomography, leveraging optical components and electromagnetically induced transparency to improve accuracy and efficiency in quantum state estimation.

Contribution

The work introduces an integrated all-optical neural network for quantum state tomography, utilizing built-in nonlinear activation to enhance measurement accuracy and mitigate errors.

Findings

Validates the all-optical setup through experimental results.

Demonstrates improved accuracy in phase parameter prediction.

Shows potential for integration into future quantum networks.

Abstract

Quantum state tomography (QST) is a crucial ingredient for almost all aspects of experimental quantum information processing. As an analog of the "imaging" technique in the quantum settings, QST is born to be a data science problem, where machine learning techniques, noticeably neural networks, have been applied extensively. In this work, we build an integrated all-optical setup for neural network QST, based on an all-optical neural network (AONN). Our AONN is equipped with built-in nonlinear activation function, which is based on electromagnetically induced transparency. Experiment results demonstrate the validity and efficiency of the all-optical setup, indicating that AONN can mitigate the state-preparation-and-measurement error and predict the phase parameter in the quantum state accurately. Given that optical setups are highly desired for future quantum networks, our all-optical setup of integrated AONN-QST may shed light on replenishing the all-optical quantum network with the last brick.