Uncertainty Quantification for Quantum Fidelity Estimation
The research introduces a hybrid Bayesian-informed conformal prediction framework to provide rigorous, distribution-free prediction intervals for quantum state fidelity estimation. This method combines Bayesian priors with split conformal prediction to create uncertainty bounds that maintain statistical validity across various experimental challenges, such as finite samples and model errors, offering a scalable tool for verifying noisy quantum hardware.