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Characterizing Arbitrary Lindbladian Dynamics with a Few Pauli Measurements

arXiv 2607.23044 ↗First posted 25 Jul 2026quant-ph cs.IT cs.LGcited by 0

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Abstract (arXiv)

Quantum devices are open systems whose dynamics interleave coherent evolution with dissipation, and benchmarking, error mitigation, and error correction all rest on a faithful model of both. Existing characterization protocols either assume prior knowledge of the interaction and noise structure, or demand ancillas, entangled probes, or mid-circuit control, or capture only the Pauli-diagonal part of the noise. Here, we present a protocol that reconstructs an arbitrary sparse Markovian generator, including every Hamiltonian together with the jump operator coefficients, using only product Pauli state preparation, single uninterrupted forward evolutions, and product Pauli measurements. Given a sparsity budget M0 and a strength bound Gamma of the Lindbladian, every coefficient is learned to precision ε from widetildeO(Gamma2M02/ε4) experiments and widetildeO(Gamma M02/ε2) total evolution time, with both supports identified from data without locality assumptions. The protocol runs at a logarithmic number of positive evolution times on a hardware clock lattice and is provably robust to calibrated state-preparation and measurement errors.

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References · 13

  1. 2016Efficient quantum tomography IIcited by 54
  2. 2020Sample-efficient learning of quantum many-body systemscited by 36
  3. 2022Learning many-body Hamiltonians with Heisenberg-limited scalingcited by 22
  4. 2023Learning quantum Hamiltonians at any temperature in polynomial timecited by 20
  5. 2022Practical Black Box Hamiltonian Learningcited by 15
  6. 2024Structure learning of Hamiltonians from real-time evolutioncited by 8
  7. 2024Learning k-body Hamiltonians via compressed sensingcited by 4
  8. 2024Learning the structure of any Hamiltonian from minimal assumptionscited by 4
  9. 2025Ansatz-free Hamiltonian learning with Heisenberg-limited scalingcited by 3
  10. 2025SPAM Tolerance for Pauli Error Estimationcited by 1
  11. 2026Optimal detection of dissipation in Lindbladian dynamicscited by 1
  12. 2026Heisenberg-limited Hamiltonian learning without short-time controlcited by 1
1 more
  1. 2026Optimal Ansatz-free Hamiltonian Learning In Situcited by 1

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