Toward a Quantum-Inspired Linear Genetic Programming Model

The huge performance superiority of quantum computers for some specific problems lies in their direct use of quantum mechanical phenomena (e.g. superposition of states) to perform computations. This has motivated the creation of quantum-inspired evolutionary algorithms (QIEAs), which successfully use some quantum physics principles to improve the performance of evolutionary algorithms (EAs) for classical computers. This paper proposes a novel QIEA (Quantum-Inspired Linear Genetic Programming - QILGP) for automatic synthesis of machine code (MC) programs and aims to present a preliminary evaluation of applying the quantum-inspiration paradigm to evolve programs by using two symbolic regression problems. QILGP performance is compared to AIMGP model, since it is the most successful genetic programming technique to evolve MC. In the first problem, the hit ratio of QILGP (100%) is greater than the one of AIMGP (77%). In the second problem, QILGP seems to carry on a less greedy search than AIMGP. Since QILGP presents some satisfactory results, this paper shows that the quantum-inspiration paradigm can be a competitive approach to evolve programs more efficiently, which encourages further developments of that first and simplest QILGP model with multiple individuals.