  DNA sequencing allows for the determination of the genetic code of an
organism, and therefore is an indispensable tool that has applications in
Medicine, Life Sciences, Evolutionary Biology, Food Sciences and Technology,
and Agriculture. In this paper, we present several novel methods of performing
classical-to-quantum data encoding inspired by various mathematical fields, and
we demonstrate these ideas within Bioinformatics. In particular, we introduce
algorithms that draw inspiration from diverse fields such as Electrical and
Electronic Engineering, Information Theory, Differential Geometry, and Neural
Network architectures. We provide a complete overview of the existing data
encoding schemes and show how to use them in Genomics. The algorithms provided
utilise lossless compression, wavelet-based encoding, and information entropy.
Moreover, we propose a contemporary method for testing encoded DNA sequences
using Quantum Boltzmann Machines. To evaluate the effectiveness of our
algorithms, we discuss a potential dataset that serves as a sandbox environment
for testing against real-world scenarios. Our research contributes to
developing classical-to-quantum data encoding methods in the science of
Bioinformatics by introducing innovative algorithms that utilise diverse fields
and advanced techniques. Our findings offer insights into the potential of
Quantum Computing in Bioinformatics and have implications for future research
in this area.
