Quantum Network Tomography with Multi-party State Distribution
Quantum Network Tomography with Multi-party State Distribution
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DOI：
10.1109/qce53715.2022.00061
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
M. G. Andrade;Jaime Diaz;Jake Navas;S. Guha;I. Montaño;Brian J. Smith;M. Raymer;D. Towsley
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文献类型：
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作者：
M. G. Andrade;Jaime Diaz;Jake Navas;S. Guha;I. Montaño;Brian J. Smith;M. Raymer;D. Towsley
量子信息的脆弱本质使得在量子信道传输中完全将量子态与噪声隔离开来实际上是不可能的。量子网络是由量子处理设备通过量子通道相互连接而形成的复杂系统。在这种情况下，表征信道如何在传输量子态中引入噪声是至关重要的。对非单一量子信道引入的误差分布的精确描述可以通知量子纠错协议针对特定的误差模型定制操作。此外，通过端到端测量监测网络来表征此类错误，使终端节点能够推断网络链路的状态。在这项工作中，我们通过引入量子网络断层扫描问题来解决量子网络中量子通道的端到端表征。该问题的解决方案是对网络中所有量子通道定义克劳斯分解的参数进行估计，使用仅在终端节点进行的测量。我们详细研究了任意星型量子网络中由单个泡利算子描述的量子信道的情况，如位翻转量子信道。我们为这类具有多项式样本复杂度的网络提供了解决方案。我们的解决方案提供了证据，证明预共享纠缠在参数可识别性方面为估计带来了优势。
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.