AI 中文总结
针对直接保真度估计在非克利福德门场景下开销过高的问题，提出联合基准分组方法，推导相关估计量并将其应用于强化学习门校准，实现低开销的上下文保留保真度估计。
AI 中文摘要
容错量子计算依赖于低物理错误率的要求，要达到阈值以下需考虑电路相关噪声，这种噪声是量子门通常嵌入的执行环境所固有的。直接保真度估计是一种能自然保留上下文的技术，它仅需在感兴趣的窗口周围插入局域泡利制备和测量基准。然而，每个采样的输入输出泡利对都需要各自的制备和测量设置，一旦目标门不再是克利福德门，这种开销会迅速增加。我们提出联合基准分组，它将泡利对划分为输入和输出算子可对易的集合，从而能在同一制备测量设置下估计多个泡利转移系数。我们推导了无偏分组估计量和有限样本保证，表明分组总能减少不同输入输出设置的数量，且当目标权重集中在兼容组内时，还能减少所需的信道使用次数。我们针对参数化两量子比特门$\text{fSim}(\theta,\boldsymbol{\theta})$表征了这些增益，并将分组估计量用作基于强化学习的门校准的上下文敏感奖励。我们的结果为连续参数化量子门的低开销、上下文保留的保真度估计提供了实用途径。
英文摘要
Fault-tolerant quantum computation hinges on the requirement for low physical error rates. Reaching below threshold regime requires the accounting of circuit dependent noise, that is inherent to the execution context in which a quantum gate is usually embedded. Direct fidelity estimation is a technique that offers natural context preservation as it solely requires the insertion of local Pauli preparation and measurement fiducials around the window of interest. However, each sampled input-output Pauli pair demands its own preparation and measurement setting, an overhead that grows rapidly once the target gate is no longer Clifford. We introduce joint fiducial grouping, which partitions Pauli pairs into sets with commuting input and output operators, allowing several Pauli-transfer coefficients to be estimated within the same preparation-measurement setting. We derive an unbiased grouped estimator and finite-sample guarantees showing that grouping always reduces the number of distinct input-output settings and can also reduce the required channel uses when the target weight is concentrated within compatible groups. We characterize these gains for the parametric two-qubit gate $\mathrm{fSim}(θ,φ)$, and use the grouped estimator as a context-sensitive reward for reinforcement-learning-based gate calibration. Our results provide a practical route to lower-overhead, context-preserving fidelity estimation for continuously parameterized quantum gates.
Comments18 pages, 5 figures
