Assumption-free fidelity bounds for hardware noise characterization
This addresses the challenge of noise characterization for quantum computing hardware, particularly in the Quantum Supremacy regime, offering a practical solution for error estimation when classical methods fail.
The paper tackles the problem of estimating hardware noise in quantum computers, where classical simulations become infeasible, by using machine learning and conformal prediction to provide theoretically valid upper bounds on fidelity between noiseless and noisy outputs, achieving results that apply broadly without requiring noise modeling.
In the Quantum Supremacy regime, quantum computers may overcome classical machines on several tasks if we can estimate, mitigate, or correct unavoidable hardware noise. Estimating the error requires classical simulations, which become unfeasible in the Quantum Supremacy regime. We leverage Machine Learning data-driven approaches and Conformal Prediction, a Machine Learning uncertainty quantification tool known for its mild assumptions and finite-sample validity, to find theoretically valid upper bounds of the fidelity between noiseless and noisy outputs of quantum devices. Under reasonable extrapolation assumptions, the proposed scheme applies to any Quantum Computing hardware, does not require modeling the device's noise sources, and can be used when classical simulations are unavailable, e.g. in the Quantum Supremacy regime.