SPLiT: Channel-Aware Learning for Quantum Error Mitigation

Quantum error mitigation (QEM) improves observable estimates on noisy quantum devices. Circuit-specific estimation and explicit channel inversion incur additional execution, characterization, or sampling costs. Direct learned regressors enable fast inference on new circuits but leave noise accumulation implicit, which can impair accuracy under depth extrapolation and device drift. We propose SPLiT (Sparse Pauli–Lindblad Transformer)), which reformulates learned mitigation around explicit layerwise noise channels. A causal Transformer predicts channel rates, from which we construct a survival prior that accumulates analytically across layers. Observable-conditioned residuals refine this shared prior into deterministic mitigation coefficients for individual Pauli terms. A differentiable qubit-transfer dynamic program evaluates the prior exactly for open-chain channels with per-layer cost linear in qubit count. Relative-position attention supports depth extrapolation, while calibration and measurement conditioning adapt predictions to noise drift without retraining. Across a simulation sweep of four circuit families spanning structured and random circuits under three noise models, SPLiT achieves the lowest mean absolute error (MAE) in $10$ of $12$ settings, with $1.3$–$2.3\times$ lower MAE on multi-Pauli energy tasks than the strongest baseline in each setting. It also achieves the lowest reported MAE under temporal drift, beyond training depths, and on a $100$-qubit benchmark.