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.
Fast-forwardable lindbladians imply quantum phase estimation.arXiv preprint arXiv:2510.06759,
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Purely dissipative Lindbladians without Hamiltonian part can approximate unitary dynamics to ε error in diamond norm with O(t²/ε) time, which is optimal for time-independent cases.
Introduces Amplitude-Phase Separation (APS) decomposition for quantum simulation of non-unitary dynamics, with complementary error scaling advantages in time-independent cases and unification of prior methods like LCHS and NDME.
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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Hamiltonian dynamics from pure dissipation
Purely dissipative Lindbladians without Hamiltonian part can approximate unitary dynamics to ε error in diamond norm with O(t²/ε) time, which is optimal for time-independent cases.
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Quantum Simulation of Non-Unitary Dynamics via Amplitude-Phase Separation
Introduces Amplitude-Phase Separation (APS) decomposition for quantum simulation of non-unitary dynamics, with complementary error scaling advantages in time-independent cases and unification of prior methods like LCHS and NDME.