🤖 AI Summary
This work addresses the challenge of characterizing quantum device dynamics, particularly non-diagonal dissipative processes, which existing methods struggle to capture effectively due to their reliance on prior noise models, ancillary qubits, or complex control sequences. The authors propose an efficient reconstruction scheme requiring only product Pauli initial state preparation, a single uninterrupted evolution, and product Pauli measurements. Notably, the method identifies the support of a sparse Lindbladian without assuming locality. Leveraging compressed sensing principles, it robustly reconstructs all coefficients of the Hamiltonian and jump operators with $\tilde{O}(\Gamma^2 M_0^2 / \varepsilon^4)$ experimental repetitions and $\tilde{O}(\Gamma M_0^2 / \varepsilon^2)$ total evolution time to achieve accuracy $\varepsilon$, while providing theoretical robustness guarantees against calibrated initialization and measurement errors.
📝 Abstract
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 $M_0$ and a strength bound $Γ$ of the Lindbladian, every coefficient is learned to precision $ε$ from $\widetilde{O}(Γ^2M_0^2/ε^4)$ experiments and $\widetilde{O}(ΓM_0^2/ε^2)$ 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.