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.

翻译：DNA测序能够确定生物体的遗传密码，因此是医学、生命科学、进化生物学、食品科学与技术以及农业等领域不可或缺的工具。本文受多个数学领域的启发，提出了几种将经典数据编码为量子数据的新方法，并将这些想法应用于生物信息学领域。具体而言，我们引入了借鉴电气与电子工程、信息论、微分几何和神经网络架构等不同领域的算法。我们全面概述了现有的数据编码方案，并展示了如何在基因组学中使用它们。所提供的算法利用了无损压缩、基于小波的编码和信息熵。此外，我们提出了一种使用量子玻尔兹曼机测试已编码DNA序列的现代方法。为了评估算法的有效性，我们讨论了一个潜在数据集，该数据集可作为沙盒环境用于测试现实场景。我们的研究通过引入利用不同领域和先进技术的创新算法，为生物信息学科学中的经典到量子数据编码方法的发展做出了贡献。我们的研究结果揭示了量子计算在生物信息学中的潜力，并对该领域的未来研究具有启示意义。