无需假设即可准确评估量子设备噪声下的保真度上限。
Assumption-free fidelity bounds for hardware noise characterization
- 结合机器学习与置信预测，实现无假设的保真度上界估计。
- 在量子优越性场景下，即使无法经典模拟也能有效估算误差。
- 适用于任何硬件，无需建模噪声源，适合实际量子设备验证。
在量子优越性阶段，若能估计、缓解或纠正不可避免的硬件噪声，量子计算机可能在若干任务上超越经典计算。但估计误差需依赖经典模拟，而此类模拟在量子优越性阶段变得不可行。本文利用机器学习数据驱动方法与置信预测（Conformal Prediction），一种以弱假设和有限样本有效性著称的不确定性量化工具，为量子设备无噪声输出与有噪声输出之间的保真度提供理论有效的上界。在合理外推假设下，该方法可应用于任意量子计算硬件，无需建模设备噪声来源，且在经典模拟不可用时（如量子优越性阶段）仍可使用。
原文摘要 · Abstract (English)
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.
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