All-optical neural network quantum state tomography

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 alloptical quantum network with the last brick.

Linear-depth quantum circuits for loading Fourier approximations of arbitrary functions

“…Note that due to the high cost of running a quantum computer, performing similar quantum state tomography experiments to verify the accuracy of the FSL method is not a viable option for more than three qubits. Even though there are economical algorithms to perform state tomography using randomized measurements [44,45] or machine learning techniques [46][47][48][49], they are beyond the scope of this work.…”