  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 defining so a
Quantum Case-Based Reasoning (qCBR) paradigm. 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.
