Establishes n^{Ω(k)} lower bounds for learning k-local Hamiltonians from time evolution, including single-coefficient and effective Hamiltonian learning, via a new connection to Boolean function analysis.
Improved hamiltonian learning and sparsity testing through bell sampling.arXiv preprint arXiv:2509.07937, 2025
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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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Lower Bounds for Learning Hamiltonians from Time Evolution
Establishes n^{Ω(k)} lower bounds for learning k-local Hamiltonians from time evolution, including single-coefficient and effective Hamiltonian learning, via a new connection to Boolean function analysis.
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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.