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Quantum algorithms for causal estimands

delete2025-12-12
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R
Rishi S Goel *
C
Casey R. Myers
S
Sally Shrapnel
DOI：10.1088/2058-9565/ae20b8delete
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摘要

摘要

En 中文
现代机器学习（ML）方法通常无法充分捕捉因果关系信息。因此，这类模型无法处理数据分布的偏移，易受对抗样本影响，并且常常学习到虚假的相关性（Schölkopf and von Kügelgen 2022 (arXiv:2204.00607) [cs.AI]）。因果机器学习，或因果推断，旨在通过利用观测数据和/或干预数据来估计反事实事件的结果，以解决这些问题，其中因果关系通常被表示为有向无环图。这些因果算法是否能为量子增强提供机会，目前尚无定论。在本文中，我们考虑了一类最近开发的非参数化、连续因果估计器，并为此类任务推导了量子算法。核函数评估和大矩阵求逆是这些经典算法的关键子程序，这使得它们特别适合量子处理。与其他量子机器学习算法不同，这些估计器存在封闭形式解，无需进行梯度评估和变分学习。我们描述了几种新的混合量子-经典算法，并证明估计器的统一一致性得以保持。此外，如果能够接受一个与真实因果估计量成比例的量子态输出，那么这些算法将继承量子线性系统求解器所提供的指数级复杂度优势。
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期刊

Quantum Science and Technology 封面图
Quantum Science and Technology
IF:
5
论文数:
1.4K
被引数:
5.1K

机构

S
school of mathematics and physics, the university of queensland, st. lucia, brisbane, qld 4072, australia
学者数:
2
论文数: 1
被引数: 0
S
school of physics, mathematics and computing, the university of western australia, crawley, wa 6009, australia
学者数:
1
论文数: 1
被引数: 0
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