Spatiotemporal quantum advantages for rare-event sampling
Abstract
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.
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BibTeX
@misc{gupta2026spatiotemporal,
title = {{Spatiotemporal quantum advantages for rare-event sampling}},
author = {Akshat Gupta and Jianjun Chen and Chengran Yang and Mile Gu and Caterina Doglioni and Jayne Thompson and Thomas J. Elliott},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.03026},
eprint = {2610.03026},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.03026},
}
RIS
TY - UNPB
TI - Spatiotemporal quantum advantages for rare-event sampling
AU - Akshat Gupta
AU - Jianjun Chen
AU - Chengran Yang
AU - Mile Gu
AU - Caterina Doglioni
AU - Jayne Thompson
AU - Thomas J. Elliott
PY - 2026
DA - 2026/10/02
PB - arXiv
DO - 10.48550/arxiv.2610.03026
UR - https://arxiv.org/abs/2610.03026
AB - 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.
ER -