Quantum case-based reasoning (qCBR)

Parfait Atchade Adelomou, Daniel Casado Fauli, Elisabet Golobardes Ribé, Xavier Vilasís-Cardona

Research output: Indexed journal article Articlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)2639-2665
Number of pages27
JournalArtificial Intelligence Review
Issue number3
Publication statusPublished - Mar 2023


  • Artificial intelligent
  • Case-based reasoning
  • Machine learning
  • Quantum case-based reasoning
  • Quantum computing
  • Variational quantum classifier
  • Vqc


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