A Theoretical Proposal for Ququart-Based QuantumComputing Architecture for Native Quaternary (A,T, G, C) Genomic Processing and De Novo GeneSynthesis

This conceptual paper proposes a novel quantum computing architecture specifically engineered for native genomic processing and generative synthetic biology. Traditional bioinformatics frameworks rely on binary silicon-based architectures, which introduce significant computational overhead and data structural loss when translating the quaternary biological alphabet (Adenine, Thymine, Guanine, Cytosine) into binary states (0 and 1). To resolve this bottleneck, this theoretical proposal introduces a hardware and software paradigm utilizing 4-level quantum states—known as ququarts or d=4 qudits. This architecture establishes a 1:1 direct isomorphic mapping between the four distinct energy levels of a single quantum particle and the four biological nucleotides (A, T, G, C). Key concepts introduced in this paper include: Thermodynamic-Isomorphic Mapping: Calibrating the energy gaps between ququart states to represent the physical hydrogen bond dissociation energies of DNA base pairs, natively embedding biological physics into the quantum hardware. Native Quantum Genomic Logic: Bypassing the traditional binary conversion layer to process genomic data natively through specialized 4x4 unitary matrices (e.g., Complementary Gates). Quaternary Genomic Large Language Models (QG-LLM): A proposed native deep learning architecture that processes 4-state quantum tensors to detect long-range regulatory patterns without binary tokenization. Generative Genetics: The theoretical application of this architecture for the de novo generation of synthetic gene sequences and the ultra-fast in silico simulation of unknown genetic functions using quantum superposition. This document serves as a formal prior art assertion and theoretical foundation for the conceptualization of native ququart-based genomic processors and their application in evolutionary and synthetic genetic synthesis.