How Quantum Mechanics Can Explain Evolutionary Adaptations and Novelty?

Quantum biology seeks to explain biological phenomena via quantum

mechanisms, such as enzyme reaction rates via tunnelling and photosynthesis

energy efficiency via coherent superposition of states. However, less

effort has been devoted to study the role of quantum mechanisms in biological

evolution. In this paper, we used transcription factor networks with two

and four different phenotypes, and used classical random walks (CRW) and

quantum walks (QW) to compare network search behaviour and efficiency

at finding novel phenotypes between CRW and QW. In the network with

two phenotypes, at temporal scales comparable to decoherence time TD,

QW are as efficient as CRW at finding new phenotypes. In the case of the

network with four phenotypes, the QW had a higher probability of mutating

to a novel phenotype than the CRW, regardless of the number of mutational

steps (i.e. 1, 2 or 3) away from the new phenotype. Before quantum decoherence,

the QW probabilities become higher turning the QW effectively more

efficient than CRW at finding novel phenotypes under different starting conditions.

Thus, our results warrant further exploration of the QW under more

realistic network scenarios (i.e. larger genotype networks) in both closed and

open systems (e.g. by considering Lindblad terms).

J. R. Soc. Interface 17: 20200567. http://dx.doi.org/10.1098/rsif.2020.0567