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
