On how neural networks enhance quantum state tomography with constrained measurements

• URL: http://arxiv.org/abs/2111.09504v2
• Date: Wed, 2 Aug 2023 04:40:11 GMT
• Title: On how neural networks enhance quantum state tomography with constrained measurements
• Authors: Hailan Ma, Daoyi Dong, Ian R. Petersen, Chang-Jiang Huang, Guo-Yong Xiang
• Abstract summary: We propose a deep neural networks based quantum state tomography (DNN-QST) approach, which are applied to three measurement-constrained cases. DNN-QST exhibits a great potential to achieve high fidelity for quantum state tomography with limited measurement resources and can achieve improved estimation when tomographic measurements suffer from noise.
• Score: 3.1866319932300953
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum state tomography aiming at reconstructing the density matrix of a quantum state plays an important role in various emerging quantum technologies. Inspired by the intuition that machine learning has favorable robustness and generalization, we propose a deep neural networks based quantum state tomography (DNN-QST) approach, which are applied to three measurement-constrained cases, including few measurement copies and incomplete measurements as well as noisy measurements. Numerical results demonstrate that DNN-QST exhibits a great potential to achieve high fidelity for quantum state tomography with limited measurement resources and can achieve improved estimation when tomographic measurements suffer from noise. In addition, the results for 2-qubit states from quantum optical devices demonstrate the generalization of DNN-QST and its robustness against possible error in the experimental devices.

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