Quantum process tomography based on distributed compressed sensing

doi:10.16511/j.cnki.qhdxxb.2017.25.050

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Abstract Quantum process tomography (QPT) is one of the foundations of quantum information science research, but the required experimental resources during QPT grow exponentially with the number of qubits. Recently, a compressed sensing QPT (CSQPT) was proposed that significantly reduces the required resources and the post-processing time based on the sparseness of the process matrix. However, the quantum channel analysis needs to simultaneously identify a variety of quantum gates and there are always outliers during the QPT process. This paper describes a distributed compressed sensing quantum process tomography (DCSQPT) method to identify the multi quantum channel tomography while effectively attenuating outliers through collaborative sparse learning. Simulations show that this method is robust to outlier data and accurately reconstructs the process matrix compared to the compressed sensing QPT method while significantly improving the quantum process tomography identification speed.

Keywords quantum process tomography distributed compressed sensing quantum channel

ZTFLH:

N945.14

Issue Date: 15 October 2017

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	YUAN Xiaohu
	WU Rebin
	LI Chunwen

Cite this article:

YUAN Xiaohu,WU Rebin,LI Chunwen. Quantum process tomography based on distributed compressed sensing[J]. Journal of Tsinghua University(Science and Technology), 2017, 57(10): 1089-1095.

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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2017.25.050 OR http://jst.tsinghuajournals.com/EN/Y2017/V57/I10/1089

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