An ansatz-free Lindbladian learning algorithm via Bell sampling with a SPAM-robust extension for gauge-independent parts of sparse Lindbladians under constant noise.
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 4representative citing papers
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.
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.
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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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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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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.