Function Optimization Based on Higher-Order Quantum Genetic Algorithm

Abstract

Quantum genetic algorithms (QGA) are typically built using the traditional representation of the quantum chromosome in the form of system of independent qubits. This makes it impossible to use a very powerful quantum calculations mechanism, namely quantum state entanglement. In this paper we implement a higher-order QGA and illustrate efficiency of the algorithm on the basis of example of optimization problem solved for a test functions set. An adaptive quantum gate operator, which does not require a lookup table is also proposed. In comparison to traditional QGA, the transition to higher (more than two) orders in the algorithm implementation shows much better results in terms of the running time, convergence speed and solution precision.