Physics-Informed Learning of Effective Error Processes from Limited Noisy Transmon Measurements for Robust QAOA Reliability

Abstract:We study whether limited finite-shot calibration measurements from a hidden transmon-inspired simulator can be used to learn compact, task-relevant effective error models for variational-algorithm reliability. Each physical element is modeled as a weakly anharmonic qutrit with coherent control imperfections, dissipation, dephasing, leakage, readout assignment error, and sampling noise, while the learner receives only selected local tomography data and, in the three-qubit extension, targeted pair probes. The learned objects are local affine Bloch response maps supplemented by process-relative edge residuals, and they are tested through their ability to reproduce and mitigate deformations of QAOA/MaxCut cost landscapes in simulation. The results show that strongly incomplete local data can still support useful response-map inference, that regularized linear models become competitive with neural networks in the scaled three-qubit setting, and that pair probes provide useful edge-level information, giving the clearest edge-residual prediction gain at the full pair-probe budget and a measurable downstream QAOA benefit. In the best non-oracle test, a Clifford-data-regression-style local-inverse correction reduces the finite-shot QAOA landscape error from $0.18187\,[0.17576,0.18798]$ to $0.01811\,[0.01751,0.01871]$, corresponding to a $10\times$ improvement. The study supports a hardware-aware, measurement-efficient calibration strategy, while incorporating leakage-explicit diagnostics and a non-oracle regression-style correction.

arXiv:2606.00353 [quant-ph]

(or arXiv:2606.00353v2 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2606.00353 Focus to learn more arXiv-issued DOI via DataCite

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