The Conquest of Quantum Genetic Algorithms: The Adventure to Cross the Valley of Death

TL;DR

This paper discusses the challenges of adapting classical genetic algorithms to quantum computing, illustrating these difficulties with a quantum version called RQGA and providing code examples.

Contribution

It introduces the RQGA, a quantum genetic algorithm, and highlights the practical challenges in translating classical AI algorithms to quantum platforms.

Findings

Quantum adaptation of genetic algorithms faces significant hurdles.

The paper provides code examples in Python and QISKIT.

Demonstrates specific setbacks in quantum algorithm implementation.

Abstract

In recent years, the emergence of the first quantum computers at a time when AI is undergoing a fruitful era has led many AI researchers to be tempted into adapting their algorithms to run on a quantum computer. However, in many cases the initial enthusiasm has ended in frustration, since the features and principles underlying quantum computing are very different from traditional computers. In this paper, we present a discussion of the difficulties arising when designing a quantum version of an evolutionary algorithm based on Darwin's evolutionary mechanism, the so-called genetic algorithms. The paper includes the code in both Python and QISKIT of the quantum version of one of these evolutionary algorithms allowing the reader to experience the setbacks arising when translating a classical algorithm to its quantum version. The algorithm studied in this paper, termed RQGA (Reduced Quantum Genetic Algorithm), has been chosen as an example that clearly shows these difficulties, which are common to other AI algorithms.