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.
Ansatz-free learning of lindbladian dynamics in situ
8 Pith papers cite this work. Polarity classification is still indexing.
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Local Lindbladians can be learned with Õ(Λ²/ε²) channel uses and Õ(Λ/ε²) total time; matching lower bounds prove this optimal even for adaptive, entangling strategies.
Ansatz-free Hamiltonian learning with product Pauli states and no control achieves optimal total evolution time Θ(Λ/ε² log(Λ/ε)), with a matching new lower bound over all control-free protocols.
Embedding experimental quantum states into high-distance codes enables exponential speedups in fault-tolerant shadow tomography and cubic observable estimation over unencoded adaptive strategies.
Ultimate precision bounds for multiparameter Markovian noise metrology show average variance scaling as Ω(1/(T R²)) with Heisenberg scaling in dissipative channels R when using entangled probes and high-rank signal correlations, attainable via rapid prepare-and-measure protocols.
A provable exponential quantum-classical learning separation is established for predicting expectation values of time-evolved quantum states under unknown low-intersection Hamiltonians, assuming BQP ⊄ P/poly.
A randomized algorithm detects dissipation of magnitude at least epsilon in unknown Lindbladian dynamics with optimal total evolution time O(epsilon^{-1}) under bounded strength and locality assumptions.
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.
citing papers explorer
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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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Near-Optimal Learning of Local Lindbladians
Local Lindbladians can be learned with Õ(Λ²/ε²) channel uses and Õ(Λ/ε²) total time; matching lower bounds prove this optimal even for adaptive, entangling strategies.
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Optimal Ansatz-free Hamiltonian Learning In Situ
Ansatz-free Hamiltonian learning with product Pauli states and no control achieves optimal total evolution time Θ(Λ/ε² log(Λ/ε)), with a matching new lower bound over all control-free protocols.
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Exponential speedups in fault-tolerant processing of quantum experiments
Embedding experimental quantum states into high-distance codes enables exponential speedups in fault-tolerant shadow tomography and cubic observable estimation over unencoded adaptive strategies.
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Precision Limits of Multiparameter Markovian-Noise Metrology
Ultimate precision bounds for multiparameter Markovian noise metrology show average variance scaling as Ω(1/(T R²)) with Heisenberg scaling in dissipative channels R when using entangled probes and high-rank signal correlations, attainable via rapid prepare-and-measure protocols.
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Provable learning separation for predicting time-evolution of quantum many-body systems
A provable exponential quantum-classical learning separation is established for predicting expectation values of time-evolved quantum states under unknown low-intersection Hamiltonians, assuming BQP ⊄ P/poly.
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Optimal detection of dissipation in Lindbladian dynamics
A randomized algorithm detects dissipation of magnitude at least epsilon in unknown Lindbladian dynamics with optimal total evolution time O(epsilon^{-1}) under bounded strength and locality 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.