Quantum Evolution in Genetic Inheritance: A Computational Framework Integrating Quantum Entanglement, Fluctuations, and Algorithmic Adaptation

We propose a quantum-theoretical model of genetic inheritance, integrating quantum fluctuations, entanglement, and an adaptive Transmission Control Protocol (TCP)-based evolutionary function. Our framework defines a genetic state density matrix that evolves via quantum operators, introducing entanglement-driven genotype correlations across generations. Additionally, we redefine natural selection as an algorithmic process, where genotype adaptation follows a feedback-regulated TCP function. Computational validation—including Monte Carlo simulations, density matrix evolution, and stochastic perturbation modeling—demonstrates that while quantum fluctuations fine-tune genetic evolution, they do not override classical selection mechanisms. These findings offer a novel paradigm for non-Mendelian inheritance, integrating quantum mechanics, information theory, and evolutionary biology.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-03-19
DOI
https://doi.org/10.5281/zenodo.15049270
Primary Topic
Evolutionary Algorithms and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Quantum Evolution in Genetic Inheritance: A Computational Framework Integrating Quantum Entanglement, Fluctuations, and Algorithmic Adaptation

Zenodo (CERN European Organization for Nuclear Research)
Evolutionary Algorithms and Applications
article

Quantum Evolution in Genetic Inheritance: A Computational Framework Integrating Quantum Entanglement, Fluctuations, and Algorithmic Adaptation

article en

Abstract

We propose a quantum-theoretical model of genetic inheritance, integrating quantum fluctuations, entanglement, and an adaptive Transmission Control Protocol (TCP)-based evolutionary function. Our framework defines a genetic state density matrix that evolves via quantum operators, introducing entanglement-driven genotype correlations across generations. Additionally, we redefine natural selection as an algorithmic process, where genotype adaptation follows a feedback-regulated TCP function. Computational validation—including Monte Carlo simulations, density matrix evolution, and stochastic perturbation modeling—demonstrates that while quantum fluctuations fine-tune genetic evolution, they do not override classical selection mechanisms. These findings offer a novel paradigm for non-Mendelian inheritance, integrating quantum mechanics, information theory, and evolutionary biology.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 76%
Evolutionary Algorithms and Applications
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