As quantum computing and networking nodes scale-up, important open questions arise on the causal influence of various sub-systems on the total system performance. These questions are related to the tomographic reconstruction of the macroscopic wavefunction and optimizing connectivity of large engineered qubit systems, the reliable broadcasting of information across quantum networks as well as speed-up of classical causal inference algorithms on quantum computers. A direct generalization of the existing causal inference techniques to the quantum domain is not possible due to superposition and entanglement. We put forth a new theoretical framework for merging quantum information science and causal inference by exploiting entropic principles. First, we build the fundamental connection between the celebrated quantum marginal problem and entropic causal inference. Second, inspired by the definition of geometric quantum discord, we fill the gap between classical conditional probabilities and quantum conditional density matrices. These fundamental theoretical advances are exploited to develop a scalable algorithmic approach for quantum entropic causal inference. We apply our proposed framework to an experimentally relevant scenario of identifying message senders on quantum noisy links. This successful inference on a synthetic quantum dataset can lay the foundations of identifying originators of malicious activity on future multi-node quantum networks. We unify classical and quantum causal inference in a principled way paving the way for future applications in quantum computing and networking.

中文翻译：

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量子熵因果推论

随着量子计算和网络节点规模的扩大，各种子系统对整个系统性能的因果影响产生了重要的开放性问题。这些问题与宏观波函数的层析成像重建和优化大型工程qubit系统的连通性，量子网络中可靠的信息广播以及量子计算机上经典因果推理算法的加速有关。由于叠加和纠缠，不可能将现有因果推理技术直接推广到量子域。通过利用熵原理，我们提出了一个融合量子信息科学和因果推理的新理论框架。第一的，我们建立了著名的量子边际问题和熵因果推论之间的基本联系。其次，受几何量子不和谐定义的启发，我们填补了经典条件概率与量子条件密度矩阵之间的空白。利用这些基本的理论进展来开发用于量子熵因果推断的可扩展算法方法。我们将我们提出的框架应用于在实验上相关的场景，以识别量子噪声链接上的消息发件人。对合成量子数据集的成功推断可以为识别未来多节点量子网络上恶意活动的发起者奠定基础。我们以有原则的方式将经典和量子因果推论统一起来，为将来在量子计算和网络中的应用铺平了道路。