Quantum case-based reasoning (qCBR)
| Autor: | Parfait Atchade Adelomou, Daniel Casado Fauli, Elisabet Golobardes Ribé, Xavier Vilasís-Cardona |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Linguistics and Language Artificial Intelligence (cs.AI) Emerging Technologies (cs.ET) Computer Science - Artificial Intelligence Artificial Intelligence Computer Science - Emerging Technologies Language and Linguistics Machine Learning (cs.LG) |
| Zdroj: | Artificial Intelligence Review. 56:2639-2665 |
| ISSN: | 1573-7462 0269-2821 |
| DOI: | 10.1007/s10462-022-10238-w |
| Popis: | 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. Comment: 17 pages, 19 figures, 9 tables |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink