论文标题

量子网络层析成像具有多党派分布

Quantum Network Tomography with Multi-party State Distribution

论文作者

de Andrade, Matheus Guedes, Días, Jaime, Navas, Jake, Guha, Saikat, Montaño, Inès, Smith, Brian, Raymer, Michael, Towsley, Don

论文摘要

量子信息的脆弱性质使实际上不可能将量子状态与量子通道传输下的噪声完全分离。量子网络是通过通过量子通道互连量子处理设备形成的复杂系统。在这种情况下,表征通道如何在传输量子状态中引入噪声至关重要。非单身量子通道引入的错误分布的精确描述可以为量子错误校正协议提供为特定误差模型量身定制操作的量子错误校正协议。此外,通过使用端到端测量来监视网络来表征此类错误,使最终节点可以推断网络链接的状态。在这项工作中,我们通过引入量子网络层析成像问题来解决量子网络中量子通道的端到端表征。解决此问题的解决方案是使用仅在最终节点中执行的测量值来定义网络中所有量子通道的kraus分解的概率的估计器。我们为任意恒星量子网络的情况详细研究了这个问题,该量子网络具有由单个Pauli操作员所描述的量子通道,例如位翼量量子通道。我们为具有多项式样本复杂性的此类网络提供解决方案。我们的解决方案提供了证据表明,预共享的纠缠在参数的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 the probabilities 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.

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