Quantum search with prior knowledge

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外文摘要：The combination of contextual side information and search is a powerful paradigm in the scope of artificial intelligence.The prior knowledge enables the identification of possible solutions but may be imperfect.Contextual information can arise naturally,for example in game AI where prior knowledge is used to bias move decisions.In this work we investigate the problem of taking quantum advantage of contextual information,especially searching with prior knowledge.We propose a new generalization of Grover's search algorithm that achieves the optimal expected success probability of finding the solution if the number of queries is fixed.Experiments on small-scale quantum circuits verify the advantage of our algorithm.Since contextual information exists widely,our method has wide applications.We take game tree search as an example.

外文关键词：

quantum computingquantum searchquantum query algorithmprior knowledge

作者：

Xiaoyu HE、Xiaoming SUN、Jialing ZHANG

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作者单位：

State Key Lab of Processors,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China

School of Computer Science and Technology,University of Chinese Academy of Sciences,Beijing 100049,China

基金：

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaStrategic Priority Research Program of Chinese Academy of Sciences

项目编号：

6232521062272441XDB28000000

出版年：

2024

DOI：

10.1007/s11432-023-3972-y

中国科学:信息科学(英文版)

中国科学院

中国科学:信息科学(英文版)

CSTPCDEI

影响因子：0.715

ISSN：1674-733X

年,卷(期)：2024.67(9)