All-optical neural network quantum state tomography
- URL: http://arxiv.org/abs/2103.06457v2
- Date: Wed, 9 Jun 2021 06:57:24 GMT
- Title: All-optical neural network quantum state tomography
- Authors: Ying Zuo, Chenfeng Cao, Ningping Cao, Xuanying Lai, Bei Zeng and
Shengwang Du
- Abstract summary: We build an integrated all-optical setup for neural network QST, based on an all-optical neural network (AONN)
Experiment results demonstrate the validity and efficiency of the all-optical setup.
Our all-optical setup of integrated AONN-QST may shed light on replenishing the all-optical quantum network with the last brick.
- Score: 0.39146761527401414
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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