在数字量子计算机上的局域动力学中观测到魔幻Mpemba效应
Observing the magic Mpemba effect in localized dynamics on a digital quantum computer
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中文总结 AI 辅助
本研究在数字量子计算机上通过相互作用诱导的退相干机制，观测到局域动力学中的魔幻Mpemba效应，证明初始魔幻较少的态可超越并更早饱和，为量子资源弛豫提供新途径。
中文摘要 AI 辅助
Mpemba效应挑战了接近平衡态的态必然更快弛豫的直觉。我们提出一个问题：在量子魔幻（非稳定化子）的产生过程中，是否会发生类似的魔幻排序反转？量子魔幻是通用量子计算的关键资源：一个初始魔幻较少的态能否超越初始魔幻较多的态，并更早达到其渐近值？在此，我们揭示了相互作用诱导的退相干是这种魔幻Mpemba效应的一种独特机制，它既不需要输运也不需要常规热化。对于相互作用$\u2113$-bit模型中的倾斜乘积态，随机相位分析预测了魔幻排序的反转：初始魔幻较少的态达到更高的饱和魔幻值，且与其空间模式无关。$\u2113$-bit模型和局域自旋链的精确模拟进一步表明，这些态也更早饱和。我们在IBM超导量子处理器上观察到了这两个特征。一种有效的$\u2113$-bit实现通过将时间编码在门角度中，在固定电路深度下获得较长的退相干时间，而微观自旋模型在局域化区域中普遍表现出反转，但在遍历区域中仅选择性地表现出反转。这些结果确立了相互作用诱导的退相干作为超越输运驱动的热化动力学的异常量子资源弛豫的途径。
英文摘要
The Mpemba effect challenges the intuition that states closer to equilibrium must relax faster. We ask whether an analogous magic-ordering reversal can occur in the generation of quantum magic (nonstabilizerness), a key resource for universal quantum computation: can a state with less initial magic overtake one with more and reach its asymptotic value sooner? Here, we uncover interaction-induced dephasing as a distinct mechanism for this magic Mpemba effect, requiring neither transport nor conventional thermalization. For tilted product states in an interacting $\ell$-bit model, a random-phase analysis predicts a reversal of the magic ordering: states with less initial magic attain higher saturated magic, independent of their spatial pattern. Exact simulations of the $\ell$-bit model and localized spin chains further show that these states also saturate earlier. We observe both features on IBM superconducting quantum processors. An effective $\ell$-bit implementation accesses long dephasing times at fixed circuit depth by encoding time in gate angles, while a microscopic spin model exhibits the reversal universally across initial patterns in the localized regime but only selectively in the ergodic regime. These results establish interaction-induced dephasing as a route to anomalous quantum-resource relaxation beyond transport-driven thermalizing dynamics.
发表机构
- National University of Singapore（新加坡国立大学）
- Shanghai University（上海大学）
- Princeton University（普林斯顿大学）
机构由 AI 辅助整理，请以论文原文为准。
