Quantum case-based reasoning (qCBR)

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Abstract

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.

AI/ML

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