Quantum case-based reasoning (qCBR)

被引：0
作者：
Parfait Atchade Adelomou
Daniel Casado Fauli
Elisabet Golobardes Ribé
Xavier Vilasís-Cardona
机构：
[1] Research Group on Data Science for the Digital Society La Salle - Universitat Ramon Llull,
[2] Lighthouse Disruptive Innovation Group,undefined
来源：
Artificial Intelligence Review | 2023年 / 56卷
关键词：
Quantum computing; Machine learning; Case-based reasoning; Quantum case-based reasoning; Artificial intelligent; VQC; Variational quantum classifier;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Case-Based Reasoning (CBR) is an artificial intelligence approach to problem-solving with a good record of success. This article proposes using Quantum Computing to improve some of the key processes of CBR, such that a quantum case-based reasoning (qCBR) paradigm can be defined. The focus is set on designing and implementing a qCBR based on the variational principle that improves its classical counterpart in terms of average accuracy, scalability and tolerance to overlapping. A comparative study of the proposed qCBR with a classic CBR is performed for the case of the social workers’ problem as a sample of a combinatorial optimization problem with overlapping. The algorithm’s quantum feasibility is modelled with docplex and tested on IBMQ computers, and experimented on the Qibo framework.
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页码：2639 / 2665
页数：26
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