quantum Case-Based Reasoning (qCBR)

TL;DR

This paper introduces Quantum Case-Based Reasoning (qCBR), leveraging quantum computing to enhance accuracy, scalability, and overlap tolerance in problem-solving, demonstrated through a comparative study on the Social Workers' Problem.

Contribution

It proposes a novel qCBR paradigm based on the variational principle, integrating quantum computing to improve classical CBR performance and feasibility.

Findings

qCBR shows improved accuracy over classical CBR

Enhanced scalability and overlap tolerance demonstrated

Feasibility confirmed on IBMQ and Qibo frameworks

Abstract

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.