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Ansatz-Free Learning of Lindbladian Dynamics In Situ
Quantum Physics · Submitted 5 Mar 2026
abstract compiled · 2026-10-06 · no language-model review
The authors' main claim Together, this provides a systematic route to scalable characterization of open-system quantum dynamics, especially in settings where the error mechanisms of interest are unknown.
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The abstract, sentence by sentence
Characterizing the dynamics of open quantum systems at the level of microscopic interactions and error mechanisms is essential for calibrating quantum hardware, designing robust simulation protocols, and developing tailored error-correction methods. Under Markovian noise/dissipation, a natural characterization approach is to identify the full Lindbladian generator that gives rise to both coherent (Hamiltonian) and dissipative dynamics. Prior protocols for learning Lindbladians from dynamical data assumed pre-specified interaction structure, which can be restrictive when the relevant noise channels or control imperfections are not known in advance. In this paper, we present a sample-efficient protocol for learning sparse Lindbladians without assuming any a priori structure. Our protocol is ancilla-free, uses only product-state preparations and Pauli-basis measurements, admits provably stable coefficient reconstruction, and achieves near-optimal time resolution, making it compatible with near-term experimental capabilities. Together, this provides a systematic route to scalable characterization of open-system quantum dynamics, especially in settings where the error mechanisms of interest are unknown.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Characterizing the dynamics of open quantum systems at the level of microscopic interactions and error mechanisms is essential for calibrating quantum hardware, designing robust simulation protocols, and developing tailored error-correction methods. | background 1.00 | 0.04 | 0.03 | asserted 1.00 |
| s1 | Under Markovian noise/dissipation, a natural characterization approach is to identify the full Lindbladian generator that gives rise to both coherent (Hamiltonian) and dissipative dynamics. | background 1.00 | 0.05 | 0.03 | asserted 0.95 |
| s2 | Prior protocols for learning Lindbladians from dynamical data assumed pre-specified interaction structure, which can be restrictive when the relevant noise channels or control imperfections are not known in advance. | gap 0.94 | 0.03 | 0.03 | asserted 0.95 |
| s3 | In this paper, we present a sample-efficient protocol for learning sparse Lindbladians without assuming any a priori structure. | contribution 1.00 | 0.94 | 0.62 | asserted 1.00 |
| s4 | Our protocol is ancilla-free, uses only product-state preparations and Pauli-basis measurements, admits provably stable coefficient reconstruction, and achieves near-optimal time resolution, making it compatible with near-term experimental capabilities. | method 0.63 | 0.94 | 0.23 | asserted 1.00 |
| s5 | Together, this provides a systematic route to scalable characterization of open-system quantum dynamics, especially in settings where the error mechanisms of interest are unknown. | interpretation 0.99 | 0.53 | 0.67 | asserted 0.93 |
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The paper
Original abstract
Characterizing the dynamics of open quantum systems at the level of microscopic interactions and error mechanisms is essential for calibrating quantum hardware, designing robust simulation protocols, and developing tailored error-correction methods. Under Markovian noise/dissipation, a natural characterization approach is to identify the full Lindbladian generator that gives rise to both coherent (Hamiltonian) and dissipative dynamics. Prior protocols for learning Lindbladians from dynamical data assumed pre-specified interaction structure, which can be restrictive when the relevant noise channels or control imperfections are not known in advance. In this paper, we present a sample-efficient protocol for learning sparse Lindbladians without assuming any a priori structure. Our protocol is ancilla-free, uses only product-state preparations and Pauli-basis measurements, admits provably stable coefficient reconstruction, and achieves near-optimal time resolution, making it compatible with near-term experimental capabilities. Together, this provides a systematic route to scalable characterization of open-system quantum dynamics, especially in settings where the error mechanisms of interest are unknown.
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How the literature uses it
12 arXiv papers cite this in 33 sentences
Papers citing it · 18 sentences without a confident label
Compares with it 5 papers
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This result matches the $ϵ$-dependence of the evolution time posited in [ref] , allowing us to efficiently learn the Lindbladian without requiring exponential classical postprocessing at the cost of only an additional factor of $𝒪(M)$.
Efficient and SPAM-Robust Ansatz-Free Lindbladian Learning · Full Noiseless Learning Algorithm · 0.93
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The only existing ansatz-free protocol for full Lindbladian learning incurs a structure-identification cost of $1/ε^4$, preventing an end-to-end SQL guarantee [ref] .
Learning Arbitrary Lindbladians with Quantum Error Correction · Introduction · 0.89
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It is natural to ask whether one can further relax this requirement to a constant time resolution, or conversely prove that some form of short-time resolution is fundamentally necessary for control-free Hamiltonian learning, analogous to the resolution barrier recently observed in ansatz-free Lindbladian learning [ref] .
Optimal Ansatz-free Hamiltonian Learning In Situ · Discussion · 0.86
Cites it as background 5 papers
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In recent years, a plethora of protocols for learning the Liouvillian of an experimental quantum system have been proposed [ref] and implemented [ref] .
Pairwise Liouvillian learning from randomized measurements: practical aspects and guidelines for operating the protocol in large-scale experiments · Introduction · 0.93
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A range of approaches for learning Lindbladians has been proposed, including convex optimization [ref] , steady-state methods [ref] , and derivative estimation [ref] .
Efficient and SPAM-Robust Ansatz-Free Lindbladian Learning · Introduction · 0.92
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More recently, the problem of learning Lindbladian dynamics themselves has also gathered significant attention [ref] .
Near-Optimal Learning of Local Lindbladians · Introduction · 0.94
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