Useful entanglement can be extracted from noisy graph states

• URL: http://arxiv.org/abs/2402.00937v1
• Date: Thu, 1 Feb 2024 19:00:05 GMT
• Title: Useful entanglement can be extracted from noisy graph states
• Authors: Konrad Szyma\'nski, Lina Vandr\'e, Otfried G\"uhne
• Abstract summary: Cluster states and graph states in general offer a useful model of the stabilizer formalism. We leverage both properties to design feasible families of states that can be used as robust building blocks of quantum computation. We show that robust entanglement can be extracted by proper design of the linear graph with only a minimal overhead of the physical qubits.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 which can be observed during measurement. Those outcomes which are compatible 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 may 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.

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