发表机构
Nanyang Technological University; University of Manchester（南洋理工大学; 曼彻斯特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对复杂随机系统的未来概率推断问题，提出量子算法方法，在保持二次加速的同时减少系统偏差与内存成本，推进了量子技术的实际应用。
AI 中文摘要
许多潜在的未来事件会产生毁灭性后果，从地震的发生到金融黑天鹅事件，准确预测其发生概率至关重要。然而，随着此类事件背后的随机系统日益复杂，这些概率可能依赖于不断增长的过往数据，需要更多内存来跟踪。我们估计的准确性最终受计算和内存约束限制：前者用于减少采样误差，后者用于避免采样偏差。传统量子算法（如振幅估计）仅解决前者，且在量子内存极为宝贵的近未来量子设备中变得难以实施。本文提供量子算法手段以减少系统偏差，同时保持二次加速。我们的结果因此推进了量子技术实际部署的时间线，并展示了其在模拟日益复杂过程时的额外优势。
英文摘要
Many potential future events have devastating consequences, from the onset of earthquakes to financial black swan events. Accurate predictions of their probability of occurrence are vital. Yet as the stochastic systems underlying such events become increasingly complex, these probabilities can depend on ever-growing amounts of past data, which require ever more memory to track. The accuracy of our estimates is ultimately limited by both computational and memory constraints; the former to reduce sampling error, the latter to avoid sampling bias. Traditional quantum algorithms -- such as amplitude estimation -- address only the former, and become untenable especially in near-future quantum devices where quantum memory is a premium. Here, we offer quantum-algorithmic means to reduce systematic bias while maintaining a quadratic speed-up. Our results thus advance the timeline for the practical deployment of quantum technologies, and illustrate their additional benefits when simulating processes of growing complexity.
