quantum Case-Based Reasoning (qCBR)
This work addresses combinatorial optimization problems with overlapping, such as the Social Workers' Problem, by proposing a quantum-enhanced method, though it appears incremental as it builds on existing CBR with quantum adaptations.
The authors tackled the problem of improving Case-Based Reasoning (CBR) by integrating quantum computing, resulting in a Quantum Case-Based Reasoning (qCBR) paradigm that enhances average accuracy, scalability, and tolerance to overlapping compared to 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.