稀有事件采样的时空量子优势
Spatiotemporal quantum advantages for rare-event sampling
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中文总结 AI 辅助
针对稀有事件采样耗时难题，提出基于量子随机模型并融合量子振幅放大的方法，实现时空复杂度较经典蒙特卡洛方法的双重降低。
中文摘要 AI 辅助
在一个充满不确定性的世界中，我们有责任考虑最灾难性的可能结果，以便最好地减轻其影响。这些通常属于极端风险事件——后果严重但概率较低。然而，此类事件的稀有性限制了我们对它们的研究能力；可供借鉴的过去发生事例很少，且从模型中采样此类稀有事件通常耗时。在此，我们提出一种基于量子随机模型的稀有事件采样方法。该方法将量子随机建模的记忆优势——及其相关的制备量子样本态的高效手段——与量子振幅放大的“加速”相结合，与基于经典蒙特卡洛的方法相比，同时在空间和时间计算复杂度上实现降低。
英文摘要
In a world fraught with uncertainty, it behooves us to consider the most disastrous possible outcomes, in order that we can best mitigate against them. Often these are extreme risk events -- of high consequence, but low probability. Yet, the rarity of such events limits our ability to study them; there is a paucity of past occurrences to draw upon, and sampling such rare events from models is typically costly in time. Here, we introduce an approach for rare-event sampling based on quantum stochastic models. This integrates the memory advantages of quantum stochastic modelling -- and its associated efficient means of preparing quantum sample states -- with the `speed-ups' of quantum amplitude amplification, to yield simultaneous reductions in spatial and temporal computational complexity compared to classical Monte Carlo-based approaches.
发表机构
- University of Manchester（曼彻斯特大学）
- Nanyang Technological University（南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。
