Quantum

Uncertainty Quantification for Quantum Fidelity Estimation

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Briefings

A new hybrid Bayesian-informed conformal prediction framework addresses uncertainty in quantum fidelity estimation by offering distribution-free prediction intervals. The study evaluates robustness against finite-sample noise, tomographic incompleteness, physical model error, and out-of-distribution data using both classical and neural net point estimators. Results show conformalized estimators maintain statistical validity where traditional methods fail, with class-conditional conformal prediction identified as the most effective variant for approximating confidence intervals.

Key points

• The paper introduces a hybrid Bayesian-informed conformal prediction framework to provide distribution-free prediction intervals for quantum state fidelity estimation.
• Class-conditional conformal prediction was identified as the best performing variant, as it most closely approximates the conditional coverage desired for a true confidence interval.

Context and limitations

The study evaluates methodology across diverse experimental challenges including finite-sample noise, tomographic incompleteness, physical model error, and out-of-distribution data. Results are presented as findings from a preprint on arXiv, with no formal peer review or publication in a peer-reviewed journal.

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