里德堡模拟优化中的衰减与退相干：交换速率、机制与调度设计
Decay versus dephasing in Rydberg analog optimization: exchange rate, mechanism, and schedule design
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中文总结 AI 辅助
本研究将里德堡原子模拟优化中的自发衰减与退相干作为独立维度分析，揭示二者等效交换速率及机制，提出无需噪声模拟即可调度优化的方法，降低噪声对优化性能的影响。
中文摘要 AI 辅助
针对含噪声里德堡原子优化的数值研究几乎普遍将退相干压缩为单个标量，默认自发衰减（$Γ$）与退相位（$γ$）的影响等价。本研究将二者视为独立维度，针对20个随机$N=10$图上的单位圆盘最大独立集问题，绘制了量子退火启发式算法在$(Γ,γ)$平面上的性能图谱，且每个参数点均重新优化了退火时间。平均近似比确实可坍缩为单个标量，但该标量为$u=κΓ+γ$，其中$κ=8.05\pm0.5$（统计误差）$\pm1.1$（系统误差）；各向同性的$Γ+γ$残差高出30倍，无法成立。一阶微扰理论仅通过无噪声传播即可复现$κ$，并揭示其机制：目标函数在退相位算符的基矢下是对角的，因此驱动关闭后退相位无法改变结果，且单个受驱动原子已具备$κ\simeq8.5$。因此交换速率既是平台的固有属性，也是协议的属性：驱动下降占比可使其在2.3到16.3之间变化。在固定调度下，该速率在不同系统尺寸、相互作用强度和估计量下均保持稳定。由于整个成本模型是无噪声的，无需任何含噪声模拟即可针对器件的通道混合情况调整调度：联合调谐驱动下降和扫描失谐斜坡，可在不增加硬件成本的情况下恢复约三分之一的马尔可夫损伤，且噪声感知目标函数选择的斜坡并非无噪声优化会选择的斜坡。单位速率下，衰减的代价是退相位的八倍，但实测的$T_1$需乘以其到基态的分支比（校准器件为$b\approx0.4$），这使得两个林德布拉德通道在当前工作点下的代价相当。单参数噪声模型仍然可用，前提是该参数为$u$。
英文摘要
Numerical studies of noisy Rydberg-atom optimization almost universally compress decoherence into a single scalar, silently pricing spontaneous decay ($Γ$) and dephasing ($γ$) alike. We treat the two as independent axes, mapping a quantum-annealing heuristic for unit-disk maximum independent set on 20 random $N=10$ graphs across the $(Γ,γ)$ plane, with the annealing time re-optimized at every point. The mean approximation ratio does collapse onto one scalar, but onto $u=κΓ+γ$ with $κ=8.05\pm0.5$ (stat) $\pm1.1$ (syst); the isotropic $Γ+γ$ fails by a factor of 30 in residual. First-order perturbation theory reproduces $κ$ from noiseless propagation alone and gives the mechanism: the objective is diagonal in the basis of the dephasing operator, so dephasing cannot change the answer once the drive is off, and a single driven atom already has $κ\simeq8.5$. The exchange rate is thus a property of the protocol as much as of the platform: the ramp-down fraction moves it between 2.3 and 16.3. At a fixed schedule it is stable across system sizes, interaction strengths, and estimators. Because the whole cost model is noiseless, a schedule can be tuned to a device's channel mixture without any noisy simulation: jointly tuning the drive ramp-down and the sweep's detuning ramp recovers about a third of the Markovian damage at no hardware cost, and the ramp the noise-aware objective selects is not the one noiseless optimization would choose. Per unit rate decay is eightfold the dearer channel, but the measured $T_1$ enters weighted by its branching ratio to the ground state ($b\approx0.4$ for the calibrated device), which leaves the two Lindblad channels comparably costly at a present-day operating point. One-parameter noise models remain serviceable, provided the parameter is $u$.
发表机构
- North London Collegiate School（北伦敦学院）
- KAIST（韩国科学技术院）
机构由 AI 辅助整理，请以论文原文为准。
