Development of Evolutionary Systems Based on Quantum Petri Nets

Evolutionary systems (ES) include software applications that solve problems using heuristic methods instead of the deterministic ones. The classical computing used for ES development involves random methods to improve different kinds of genomes. The mappings of these genomes lead to individuals that correspond to the searched solutions. The individual evaluations by simulations serve for the improvement of their genotypes. Quantum computations, unlike the classical computations, can describe and simulate a large set of individuals simultaneously. This feature is used to diminish the time for finding the solutions. Quantum Petri Nets (QPNs) can model dynamical systems with probabilistic features that make them appropriate for the development of ES. Some examples of ES applications using the QPNs are given to show the benefits of the current approach. The current research solves quantum evolutionary problems using quantum genetic algorithms conceived and improved based on QPN. They were tested on a dynamic system using a Quantum Discrete Controlled Walker (QDCW).

Automated summary

Quantum computing reduces the time needed to find solutions by describing and simulating a large set of individuals simultaneously, presenting potential benefits for the development of ES. QPNs can model dynamical systems with probabilistic features, making them suitable for ES development. Current research successfully tackles quantum evolutionary problems using quantum genetic algorithms on dynamic systems.