Protocol learns k-local Lindbladians to ε accuracy with Õ(n^{2k}/ε²) samples and projects to valid generators; improves to log n under sparsity assumptions.
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2026 2verdicts
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A complete workflow for pairwise extraction of Liouvillian coefficients from randomized measurements is described for two-body long-range interactions with single-body noise, including parameter guidelines to minimize reconstruction error.
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Robust Structure Learning of $k$-local Lindbladians
Protocol learns k-local Lindbladians to ε accuracy with Õ(n^{2k}/ε²) samples and projects to valid generators; improves to log n under sparsity assumptions.
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Pairwise Liouvillian learning from randomized measurements: practical aspects and guidelines for operating the protocol in large-scale experiments
A complete workflow for pairwise extraction of Liouvillian coefficients from randomized measurements is described for two-body long-range interactions with single-body noise, including parameter guidelines to minimize reconstruction error.