Testing quantum dynamics in genetic information processing

Masanari Asano, Andrei Yu. Khrennikov, Masanori Ohya +2 more

Manfred Albert Hörz

This article argues that the so‑called paradox of quantum entanglement arises only under the assumption that physical spacetime has a fixed topology and that the spacetime metric is the sole structure defining proximity, separation, and locality. I develop an alternative framework in which the fundamental order is not geometric but genetic: systems are connected through processes of generation, coupling, and coherent evolution. This genetic structure forms a dynamic topology from which multiple metrics emerge. In particular, I distinguish between a physical metric, which describes spatial or spacetime distance, and a genetic metric, which measures proximity in terms of shared process history. I show that entangled systems remain local in the genetic metric even when they are far apart in the physical metric. The entanglement paradox therefore disappears: the apparent nonlocality is an artifact of evaluating quantum correlations with respect to the wrong metric. Dynamic topology provides a coherent structural account of quantum correlations and opens new perspectives on the nature of space, information, and physical reality.

Shukhrat Toirov, Ulugbek Narmuradov

This article describes optimization processes of functions and their solutions using quantum genetic algorithms. These are continuous heuristic optimization methods based on simulated genetic mechanisms, i.e., on dynamic processes in a population such as mutation, crossover, selection, and so on. This process leads to the emergence of a new class called quantum genetic algorithms. In this article, we have presented a discussion of new classes of quantum genetic algorithms, future capabilities, and benefits.

Apoorva Patel

Robert Rosén

Valerii M. Tkachuk

Typical approaches to designing quantum genetic algorithms are based on a concept of a qubit, a two-level quantum system. But many-valued quantum logic is more perspective from the point of view of the computational power. This paper proposes a quantum genetic algorithm based on a three-level quantum system in order to accelerate evolutionary process. Simulation using a set of standard test functions proves that the given algorithm is more effective and precise than the conventional quantum genetic algorithm.