  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.
