Large-scale Lindblad learning from time-series data
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917133765574656 |
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| author | Berg, Ewout van den Mitchell, Brad Wei, Ken Xuan Malekakhlagh, Moein |
| author_facet | Berg, Ewout van den Mitchell, Brad Wei, Ken Xuan Malekakhlagh, Moein |
| contents | In this work, we develop a protocol for learning a time-independent Lindblad model for operations that can be applied repeatedly on a quantum computer. The protocol is highly scalable for models with local interactions and is in principle insensitive to state-preparation errors. At its core, the protocol forms a linear system of equations for the model parameters in terms of a set of observable values and their gradients. The required gradient information is obtained by fitting time-series data with sums of exponentially damped sinusoids and differentiating those curves. We develop a robust curve-fitting procedure that finds the most parsimonious representation of the data up to shot noise. We demonstrate the approach by learning the Lindbladian for a full layer of gates on a 156-qubit superconducting quantum processor, providing the first learning experiment of this kind. We study the effects of state-preparation and measurement errors and limitations on the operations that can be learned. For improved performance under readout errors, we propose an optional fine-tuning strategy that improves the fit between the time-evolved model and the measured data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_08165 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Large-scale Lindblad learning from time-series data Berg, Ewout van den Mitchell, Brad Wei, Ken Xuan Malekakhlagh, Moein Quantum Physics In this work, we develop a protocol for learning a time-independent Lindblad model for operations that can be applied repeatedly on a quantum computer. The protocol is highly scalable for models with local interactions and is in principle insensitive to state-preparation errors. At its core, the protocol forms a linear system of equations for the model parameters in terms of a set of observable values and their gradients. The required gradient information is obtained by fitting time-series data with sums of exponentially damped sinusoids and differentiating those curves. We develop a robust curve-fitting procedure that finds the most parsimonious representation of the data up to shot noise. We demonstrate the approach by learning the Lindbladian for a full layer of gates on a 156-qubit superconducting quantum processor, providing the first learning experiment of this kind. We study the effects of state-preparation and measurement errors and limitations on the operations that can be learned. For improved performance under readout errors, we propose an optional fine-tuning strategy that improves the fit between the time-evolved model and the measured data. |
| title | Large-scale Lindblad learning from time-series data |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2512.08165 |