Near-optimal algorithm learns local Lindbladians via finite-time probes and classical shadows with Õ(Λ²/ε²) channel uses and matching lower bounds showing dissipative terms block Heisenberg-limited scaling.
Evans, Robin Harper, and Steven T
4 Pith papers cite this work. Polarity classification is still indexing.
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quant-ph 4years
2026 4verdicts
UNVERDICTED 4representative citing papers
An ansatz-free Lindbladian learning algorithm via Bell sampling with a SPAM-robust extension for gauge-independent parts of sparse Lindbladians under constant noise.
A new randomized-sampling algorithm for ansatz-free Hamiltonian learning achieves optimal control-free evolution time Θ(Λ/ε² log(Λ/ε)) with a proven matching lower bound.
Proves that local Hamiltonians are the unique approximately conserved local observables under long-time unitary evolution with high probability, enabling efficient recovery via classical shadows on product states.
citing papers explorer
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Near-Optimal Learning of Local Lindbladians
Near-optimal algorithm learns local Lindbladians via finite-time probes and classical shadows with Õ(Λ²/ε²) channel uses and matching lower bounds showing dissipative terms block Heisenberg-limited scaling.
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Efficient and SPAM-Robust Ansatz-Free Lindbladian Learning
An ansatz-free Lindbladian learning algorithm via Bell sampling with a SPAM-robust extension for gauge-independent parts of sparse Lindbladians under constant noise.
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Optimal Ansatz-free Hamiltonian Learning In Situ
A new randomized-sampling algorithm for ansatz-free Hamiltonian learning achieves optimal control-free evolution time Θ(Λ/ε² log(Λ/ε)) with a proven matching lower bound.
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Learning Hamiltonians at Long Times
Proves that local Hamiltonians are the unique approximately conserved local observables under long-time unitary evolution with high probability, enabling efficient recovery via classical shadows on product states.