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Optimal detection of dissipation in Lindbladian dynamics

2 Pith papers cite this work. Polarity classification is still indexing.

2 Pith papers citing it
abstract

Experimental implementations of Hamiltonian dynamics are often affected by dissipative noise arising from interactions with the environment. This raises the question of whether one can detect the presence or absence of such dissipation using only access to the observed time evolution of the system. We consider the following decision problem: given black-box access to the time-evolution channels $e^{t\mathcal{L}}$ generated by an unknown time-independent Lindbladian $\mathcal{L}$, determine whether the dynamics are purely Hamiltonian or contain dissipation of magnitude at least $\epsilon$ in normalized Frobenius norm. We give a randomized procedure that solves this task using total evolution time $\mathcal{O}(\epsilon^{-1})$, which is information-theoretically optimal. This guarantee holds under the assumptions that the Lindblad generator has bounded strength and its dissipative part is of constant locality with bounded degree. Our work provides a practical method for detecting dissipative noise in experimentally implemented quantum dynamics.

fields

quant-ph 2

years

2026 2

verdicts

UNVERDICTED 2

representative citing papers

Near-Optimal Learning of Local Lindbladians

quant-ph · 2026-06-18 · unverdicted · novelty 8.0

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.

citing papers explorer

Showing 2 of 2 citing papers.

  • Near-Optimal Learning of Local Lindbladians quant-ph · 2026-06-18 · unverdicted · none · ref 3 · internal anchor

    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.

  • Efficient and SPAM-Robust Ansatz-Free Lindbladian Learning quant-ph · 2026-06-15 · unverdicted · none · ref 26 · internal anchor

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