Classical-to-Quantum Sequence Encoding in Genomics

• URL: http://arxiv.org/abs/2304.10786v1
• Date: Fri, 21 Apr 2023 07:35:49 GMT
• Title: Classical-to-Quantum Sequence Encoding in Genomics
• Authors: Nouhaila Innan and Muhammad Al-Zafar Khan
• Abstract summary: We present several novel methods of performing classical-to-quantum data encoding inspired by various mathematical fields. We introduce algorithms that draw inspiration from diverse fields such as Electrical and Electronic Engineering, Information Theory, Differential Geometry, and Neural Network architectures. We propose a contemporary method for testing encoded DNA sequences using Quantum Boltzmann Machines.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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