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Trapped Ion Quantum Computing

Neural networks for quantum state tomography with constrained measurements

arXiv
Authors: Hailan Ma, Daoyi Dong, Ian R. Petersen, Chang-Jiang Huang, Guo-Yong Xiang

Year

2021

Paper ID

6730

Status

Preprint

Abstract Read

~2 min

Abstract Words

97

Citations

N/A

Abstract

Quantum state tomography (QST) aiming at reconstructing the density matrix of a quantum state plays an important role in various emerging quantum technologies. Recognizing the challenges posed by imperfect measurement data, we develop a unified neural network(NN)-based approach for QST under constrained measurement scenarios, including limited measurement copies, incomplete measurements, and noisy measurements. Through comprehensive comparison with other estimation methods, we demonstrate that our method improves the estimation accuracy in scenarios with limited measurement resources, showcasing notable robustness in noisy measurement settings. These findings highlight the capability of NNs to enhance QST with constrained measurements.

Why This Paper Matters

  • This paper contributes to the Trapped-Ion Quantum Computing research area in the Quantum Articles archive.
  • It adds a 2021 reference point for readers tracking recent quantum research.
  • Quantum state tomography (QST) aiming at reconstructing the density matrix of a quantum state plays an important role in various emerging quantum technologies.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2111.09504 [2] arXiv https://arxiv.org/abs/2111.09504 [3] Publisher https://arxiv.org/abs/2111.09504

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