Quantum Network Tomography with Multi-party State Distribution

被引:4
作者
de Andrade, Matheus Guedes [1 ]
Diaz, Jaime [2 ]
Navas, Jake [2 ]
Guha, Saikat [3 ]
Montano, Ines [2 ]
Smith, Brian [4 ]
Raymer, Michael [4 ]
Towsley, Don [1 ]
机构
[1] Univ Massachusetts Amherst, Coll Informat & Comp Sci, Amherst, MA 01003 USA
[2] No Arizona Univ, Dept Appl Phys & Mat Sci, Flagstaff, AZ 86011 USA
[3] Univ Arizona, Coll Opt Sci, Tucson, AZ 85721 USA
[4] Univ Oregon, Dept Phys, Eugene, OR 97403 USA
来源
2022 IEEE INTERNATIONAL CONFERENCE ON QUANTUM COMPUTING AND ENGINEERING (QCE 2022) | 2022年
关键词
MULTICAST-BASED INFERENCE;
D O I
10.1109/QCE53715.2022.00061
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
The fragile nature of quantum information makes it practically impossible to completely isolate a quantum state from noise under quantum channel transmissions. Quantum networks are complex systems formed by the interconnection of quantum processing devices through quantum channels. In this context, characterizing how channels introduce noise in transmitted quantum states is of paramount importance. Precise descriptions of the error distributions introduced by non-unitary quantum channels can inform quantum error correction protocols to tailor operations for the particular error model. In addition, characterizing such errors by monitoring the network with end-to-end measurements enables end-nodes to infer the status of network links In this work, we address the end-to-end characterization of quantum channels in a quantum network by introducing the problem of Quantum Network Tomography. The solution for this problem is an estimator for parameters that define a Kraus decomposition for all quantum channels in the network, using measurements performed exclusively in the end-nodes. We study this problem in detail for the case of arbitrary star quantum networks with quantum channels described by a single Pauli operator, like bit-flip quantum channels. We provide solutions for such networks with polynomial sample complexity. Our solutions provide evidence that pre-shared entanglement brings advantages for estimation in terms of the identifiability of parameters.
引用
收藏
页码:400 / 409
页数:10
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