Efficient graph-diagonal characterization of noisy states distributed over quantum networks via Bell sampling

• URL: http://arxiv.org/abs/2512.06650v1
• Date: Sun, 07 Dec 2025 04:19:09 GMT
• Title: Efficient graph-diagonal characterization of noisy states distributed over quantum networks via Bell sampling
• Authors: Zherui Jerry Wang, Joshua Carlo A. Casapao, Naphan Benchasattabuse, Ananda G. Maity, Jordi Tura, Akihito Soeda, Michal Hajdušek, Rodney Van Meter, David Elkouss,
• Abstract summary: Graph states are an important class of entangled states that serve as a key resource for distributed information processing and communication in quantum networks.<n>We propose a protocol that utilizes a Bell sampling subroutine to characterize the diagonal elements in the graph basis of noisy graph states distributed across a network.
• Score: 0.10486135378491267
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph states are an important class of entangled states that serve as a key resource for distributed information processing and communication in quantum networks. In this work, we propose a protocol that utilizes a Bell sampling subroutine to characterize the diagonal elements in the graph basis of noisy graph states distributed across a network. Our approach offers significant advantages over direct diagonal estimation using unentangled single-qubit measurements in terms of scalability. Specifically, we prove that estimating the full vector of diagonal elements requires a sample complexity that scales linearly with the number of qubits ($\mathcal{O}(n)$), providing an exponential reduction in resource overhead compared to the best known $\mathcal{O}(2^n)$ scaling of direct estimation. Furthermore, we demonstrate that global properties, such as state fidelity, can be estimated with a sample complexity independent of the network size. Finally, we present numerical results indicating that the estimation in practice is more efficient than the derived theoretical bounds. Our work thus establishes a promising technique for efficiently estimating noisy graph states in large networks under realistic experimental conditions.

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