分布式Trotter化具有最优时间缩放的纠缠成本
Distributed Trotterization with optimal time-scaling entanglement cost
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中文总结 AI 辅助
该研究针对分布式量子模拟的纠缠成本问题，提出自适应纠缠消耗的重复直到成功协议，实现随演化时间线性缩放的最优纠缠成本，为高精度分布式量子模拟提供资源高效方案。
中文摘要 AI 辅助
分布式架构将多体动力学的量子模拟扩展至单个处理器无法企及的范围，共享纠缠介导空间分离设备间的相互作用。常规实现依赖量子隐形传态，其可通用实现非局域操作，但无论门强度如何，每门会产生固定纠缠成本。这在乘积公式模拟中效率愈发低下，因更高精度需要更多、强度逐步减弱的非局域旋转，导致高精度极限下纠缠成本发散。本文提出一种简单的“重复直到成功”协议，使纠缠消耗自适应于相互作用强度。将该原语融入分布式乘积公式，可使总纠缠成本随演化时间线性缩放，且与Trotter误差无关。量子通信复杂性得出的匹配下界证明该时间缩放是最优的，为网络处理器间的高精度量子模拟奠定了资源高效的基础。
英文摘要
Distributed architectures extend quantum simulation of many-body dynamics beyond the reach of any single processor, with shared entanglement mediating interactions between spatially separated devices. Conventional implementations rely on quantum teleportation, which provides a universal realization of nonlocal operations but incurs a fixed entanglement cost per gate, irrespective of its strength. This becomes increasingly inefficient in product formula simulation, where higher accuracy requires ever more numerous, yet progressively weaker, nonlocal rotations, causing the entanglement cost to diverge in the high-accuracy limit. Here we introduce a simple repeat-until-success protocol that makes entanglement consumption adaptive to interaction strength. Incorporating this primitive into distributed product formulas yields a total entanglement cost that scales linearly with evolution time and remains independent of Trotter error. A matching lower bound from quantum communication complexity proves this time scaling to be optimal, establishing a resource-efficient foundation for high-accuracy quantum simulation across networked processors.
