量子误差缓解：来自信息动力学

中文摘要 AI 辅助

克服实验误差是量子科学中的一个核心挑战。这一挑战在大型且复杂的量子电路中尤为严峻，因为量子信息会扩散到指数级多的路径中，使得任何直接理解误差影响的尝试都变得徒劳。在本工作中，我们提出了一种理解和缓解实验误差的新方法，其基础是量子电路信息动力学的一个令人惊讶的紧凑表示——反应性函数。电路反应性函数的形状决定了其对噪声的响应，以及其适用于不同误差缓解方法的程度。基于这一视角，我们引入了两种互补的方法。第一种是泡利路径零噪声外推法，它通过拟合参数化模型到电路的反应性函数，并利用该模型精确外推到零噪声。第二种是可调误差消除法，它通过滤波电路的反应性函数，在阈值局域性内消除所有信息上的噪声。关键在于，这两种方法都包含一个调节参数，允许系统性地提高其精度直至收敛。在来自已发表实验的三个电路族中，我们的方法克服了传统零噪声外推法高达30%的偏差；在均方根误差约为$10^{-2}$时，相对于概率性误差消除，它们将采样成本降低了最多达$10^3$倍。

英文摘要

Overcoming experimental errors is a central challenge in quantum science. This challenge is particularly acute in large and complex quantum circuits, where quantum information spreads into exponentially many paths, foiling any direct attempt to understand errors' impact. In this work, we present a novel approach to understanding and mitigating experimental errors, based on a surprisingly compact representation of a quantum circuit's information dynamics, the reactivity function. The shape of a circuit's reactivity function governs its noise response, and its amenability to different error mitigation methods. From this viewpoint, we introduce two complementary such methods. The first, Pauli-path zero-noise extrapolation, fits a parameterized model to a circuit's reactivity function and uses it to accurately extrapolate to zero noise. The second, tunable error cancellation, filters a circuit's reactivity function to cancel noise on all information up to a threshold locality. Crucially, both methods feature a tuning parameter that allows one to systematically improve their accuracy until convergence. Across three circuit families from published experiments, our methods overcome conventional zero-noise extrapolation biases as large as 30%; at $\sim$$10^{-2}$ root-mean-square error, they reduce sampling cost relative to probabilistic error cancellation by up to a factor of $10^3$.

发表机构

• Google Quantum AI（谷歌量子人工智能）
• Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院物理研究所）

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