在没有经典验证的情况下的可观测量估计
Observable Estimation in the Absence of Classical Verification
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中文总结 AI 辅助
研究在无经典验证时如何信任量子结果，建立独立验证量子估计框架，应用于物理模型半混沌动力学，通过量子启发式实验测试假设并增强信心，还可通过操纵噪声设置精度界限，为可信量子计算提供途径。
中文摘要 AI 辅助
量子力学的预测成功支撑了现代科学的许多领域，尽管大型相互作用量子系统的精确模拟仍超出经典计算能力。可扩展数值近似方法的显著进步使这一成功成为可能，尽管缺乏形式上的保证，但这些方法往往具有实际准确性。随着量子模拟进入这些近似方法难以应对的领域，出现了一个基本挑战：当可靠的经典基准不可用时，如何信任量子结果？在此，我们建立了一个在此情况下独立验证量子估计的框架，并证明在没有即时可用的真实解的情况下，它们在几种考虑的方法中提供了最可信的结果。我们将框架应用于一个物理模型的半混沌动力学，该模型使几种领先的经典模拟方法陷入困境，但仍可通过实验获取，部分通过引入算子洛施密特回波。我们使用量子启发式方法系统地设计了一系列实验，这些实验共同测试了基本假设，并为从量子计算机获得的可观测量估计提供了强有力的信心。然后，我们展示了如何通过仔细表征和操纵设备噪声将此框架扩展到对量子估计设置精度界限，将验证可观测量估计的问题转化为验证噪声模型。这些结果建立了一条通往独立于经典验证的、用于科学发现的可信量子计算的途径。
英文摘要
The predictive success of quantum mechanics underpins many areas of modern science, even as the exact simulation of large, interacting quantum systems remains beyond the reach of classical computation. This success has been enabled by the remarkable advancement of scalable numerical approximation methods, which often demonstrate practical accuracy despite the absence of formal guarantees. As quantum simulation pushes into regimes where these approximations struggle, a fundamental challenge arises: How can quantum outcomes be trusted when reliable classical benchmarks are unavailable? Here, we establish a framework for the independent validation of quantum estimates in this setting and present evidence that they provide the most credible result among several considered methods, in the absence of an immediately accessible ground-truth solution. We apply our framework to the semi-scrambling dynamics of a physical model that strains several leading classical simulation methods yet remains experimentally accessible, in part through our introduction of the \textit{operator Loschmidt echo}. We systematically design a series of experiments using quantum heuristics that, taken together, test the underlying assumptions and provide strong confidence in the observable estimates obtained from the quantum computer. We then show how this framework can be extended to place accuracy bounds on quantum estimates via careful characterization and manipulation of the device noise, transforming the problem of validating the observable estimation to validating the noise model. These results establish a route towards trusted quantum computation for scientific discovery, independent of classical verification.
