Assumption-free fidelity bounds for hardware noise characterization

2025-04-09

AI Summary

In the quantum supremacy regime, hardware noise becomes intractable for classical simulation, rendering conventional fidelity estimation methods invalid. This work proposes a data-driven approach to quantum hardware characterization that requires neither explicit noise modeling nor classical simulation—marking the first application of conformal prediction to this domain. Leveraging only a small number of experimental measurements, the method yields hypothesis-free, finite-sample-valid, and theoretically guaranteed upper bounds on circuit fidelity. Crucially, it circumvents the need for precise noise models or computationally prohibitive classical simulations. The framework remains both statistically rigorous and practically applicable even for quantum systems operating beyond the reach of classical emulation. By providing distribution-free, small-sample guarantees, it establishes a new paradigm for reliability verification of real-world quantum devices.

Technology Category

• Machine Learning: Quantum Machine Learning
• Constraint Satisfaction and Optimization: Satisfiability
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

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.

Problem

• Estimate hardware noise in Quantum Supremacy regime
• Provide valid fidelity bounds without classical simulations
• Apply Machine Learning to quantify quantum device errors

Innovation

• Machine Learning for noise estimation
• Conformal Prediction for uncertainty bounds
• Assumption-free fidelity upper bounds