期刊
ARTIFICIAL INTELLIGENCE REVIEW
卷 56, 期 3, 页码 2639-2665出版社
SPRINGER
DOI: 10.1007/s10462-022-10238-w
关键词
Quantum computing; Machine learning; Case-based reasoning; Quantum case-based reasoning; Artificial intelligent; VQC; Variational quantum classifier
“Case-Based Reasoning (CBR) is an artificial intelligence approach that has achieved success. This article proposes using Quantum Computing to improve CBR and presents a comparative study between quantum and classical CBR.”
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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