Useful entanglement can be extracted from noisy graph states
Konrad Szymański, Lina Vandré, Otfried Gühne
TL;DR
This work develops a stabilizer-based approach to extract high-fidelity Bell pairs and GHZ states from noisy graph states, aiming to enable robust measurement-based quantum computation with limited connectivity. It models realistic noise—uncorrelated and correlated edge noise, local Z flips, and fusion-gate imperfections—and analyzes how carefully designed measurement patterns and postselection mitigate these effects. The authors introduce fidelity and a fidelity susceptibility α to quantify robustness, and demonstrate that twisted and crazy graph structures offer improved noise resilience, including regimes where first-order noise can be canceled. The framework provides a general, connectivity-aware method for noise reduction in MBQC, with implications for scalable quantum networks and various experimental platforms.
Abstract
Cluster states and graph states in general offer a useful model of the stabilizer formalism and a path toward the development of measurement-based quantum computation. Their defining structure - the stabilizer group - encodes all possible correlations that can be observed during measurement. The measurement outcomes which are consistent with the stabilizer structure make error correction possible. Here, we leverage both properties to design feasible families of states that can be used as robust building blocks of quantum computation. This procedure reduces the effect of experimentally relevant noise models on the extraction of smaller entangled states from the larger noisy graph state. In particular, we study the extraction of Bell pairs from linearly extended graph states - this has the immediate consequence for state teleportation across the graph. We show that robust entanglement can be extracted by proper design of the linear graph with only a minimal overhead of the physical qubits. This scenario is relevant to systems in which the entanglement can be created between neighboring sites. The results shown in this work provide a mathematical framework for noise reduction in measurement-based quantum computation. With proper connectivity structures, the effect of noise can be minimized for a large class of realistic noise processes.
