Large-scale Lindblad learning from time-series data

• URL: http://arxiv.org/abs/2512.08165v1
• Date: Tue, 09 Dec 2025 01:50:14 GMT
• Title: Large-scale Lindblad learning from time-series data
• Authors: Ewout van den Berg, Brad Mitchell, Ken Xuan Wei, Moein Malekakhlagh,
• Abstract summary: We develop a protocol for learning a time-independent Lindblad model for operations that can be applied repeatedly on a quantum computer.<n>We demonstrate the approach by learning the Lindbladian for a full layer of gates on a 156-qubit superconducting quantum processor.
• Score: 0.1749935196721634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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