  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.
