The nitrogenous bases of DNA—purines (adenine, guanine) and pyrimidines (cytosine, thymine)—feature distinct ring structures that introduce non-uniform electronic, spatial, and quantum properties to the DNA double helix. We propose a quantum information model in which the purine–pyrimidine base pairs represent spin nodes in a discrete spin network. The differential π-electron delocalization, hydrogen bonding, and aromaticity inherent in these structures can be mapped onto spin orientations, entanglement states, and quantum correlation tensors in a DNA computing architecture. Embedding these base structures into a quantum spin network formalism allows for modeling of state transfer, entanglement generation, and AI-compatible quantum learning tasks using biological scaffolds. This approach aligns with developments in topological quantum computation and biomolecular quantum information theory.
The nitrogenous bases of DNA—purines (adenine, guanine) and pyrimidines (cytosine, thymine)—feature distinct ring structures that introduce non-uniform electronic, spatial, and quantum properties to the DNA double helix. We propose a quantum information model in which the purine–pyrimidine base pairs represent spin nodes in a discrete spin network. The differential π-electron delocalization, hydrogen bonding, and aromaticity inherent in these structures can be mapped onto spin orientations, entanglement states, and quantum correlation tensors in a DNA computing architecture. Embedding these base structures into a quantum spin network formalism allows for modeling of state transfer, entanglement generation, and AI-compatible quantum learning tasks using biological scaffolds. This approach aligns with developments in topological quantum computation and biomolecular quantum information theory.
In DNA computing models, the sugar–phosphate backbone exhibits structural asymmetry, where phosphate groups covalently connect the 5′ carbon of one deoxyribose to the 3′ carbon of the next, producing a directionally polarized framework. This molecular asymmetry serves not only as a fundamental basis for replication fidelity and biological transcription but also as a topological precursor for encoding quantum states. In this paper, we propose that the canonical DNA bases, embedded in this asymmetric backbone, can be modeled as qubits projected onto a Bloch sphere, whose coordinates—x, y, z—arise from the intrinsic asymmetry and base stacking interactions. These quantum models of DNA bases are elements of a two-dimensional Hilbert space H2, establishing a framework for linking DNA computation to artificial intelligence (AI), specifically through entanglement, quantum superposition, and state collapse. We explore the implications of this Bloch sphere–Hilbert space representation for information processing, quantum learning architectures, and AI-enhanced encryption systems.
DNA replication, driven by DNA polymerase, is a highly accurate yet dynamic process essential to life. This paper proposes a novel interpretation of DNA replication as a quantum-informed information processing system. We model polymerase-mediated replication as a form of biological entanglement, wherein template and daughter strands represent coherent information states. By interpreting polymerase fidelity and base-pair matching as quantum logic operations, we propose an architecture for DNA computers that mimic replication fidelity and entanglement-like behavior. These biological processes are mapped to artificial intelligence (AI) systems using probabilistic inference, self-correction, and recursive neural architectures. The proposed framework advances the field of DNA-based computation by incorporating quantum information principles and AI integration strategies.
We propose a novel theoretical framework for a hybrid DNA–quantum computing system based on electron–positron spin-entangled pairs embedded within the chiral architecture of DNA molecules. Utilizing the spin-1/2 nature of both particles, this two-qubit entanglement structure interfaces with the quantum chiral environment of DNA, enabling controlled spin interactions, photonic readout via annihilation, and logic gate operations induced by chirality shifts (e.g., B–Z DNA transitions). This model integrates concepts from chiral-induced spin selectivity (CISS), quantum entanglement, and DNA computing, offering a pathway toward bio-quantum logic gates, memory, and information transmission. We discuss potential mechanisms for initialization, operation, and measurement, supported by over twenty relevant studies across quantum physics, spintronics, and molecular biology.
This paper establishes a rigorous theoretical and analytical framework positioning the double-helix Deoxyribonucleic Acid (DNA) molecule as a functional topological quantum computing system. Traditional molecular biology fails to address the thermodynamic paradox regarding the absolute informational fidelity of genetic transmission against high thermal noise within the intracellular medium (T ≈ 310 K). We propose a quantum bio-cryptographic model where nucleotide base pairs function as highly entangled qubits protected by non-local Quantum Error Correction (QEC) stabilizer codes. By analyzing the density matrix dynamics under localized environmental perturbations via the Lindblad master equation, we demonstrate that non-local quantum correlations suppress thermal dephasing. This mathematical architecture bridges functional quantum mechanics with structural genetics, providing an analytical blueprint for advanced bio-computing and computer-verified topological data storage.
This paper proposes a novel model wherein alleles are interpreted as quantum bits (qubits) to encode polymorphic states within a DNA-based computational substrate. We demonstrate that allelic variation, when represented as quantum superpositions and entanglements, introduces functional polymorphism into DNA computers that interface with artificial intelligence (AI). By encoding alleles as qubits, we achieve multidimensional information storage, quantum parallelism, and dynamic recombination capabilities. This approach bridges genomics, quantum computation, and AI, potentially transforming bioinformatics, cryptography, and cognitive machine learning models.
DNA has been proposed as a chemical platform for computing and data storage, paving the way for building DNA-based computers. Recently, DNA has been hypothesized as an ideal quantum computer with the base pairs working as Josephson junctions. There are still major challenges to be overcome in these directions, but they do not prevent deviceful perspectives of the main problem. The present paper explores DNA base pairs as elementary units for a scalable nuclear magnetic resonance quantum computer (NMRQC). First, it presents an overview of the proton transfer (PT) mechanism during the prototropic tautomerism in the base pairs, scoring the current stage. Second, as a proof-of-principle, the paper examines these molecular structures as quantum processing units (QPUs) of a biochemical quantum device. For the model proposed here, it is theoretically demonstrated that the nuclear spins involved in the PT of base pairs can be deterministically prepared in a superposition of triplet states. Under appropriate conditions, the proton dynamics provides the minimal two-qubit entanglement required for quantum computing. The dynamics between the canonical and tautomeric quantum states (CQS and TQS, respectively) is determined from a thermally dependent Watson-Crick quantum superposition (WCQS); i.e., |WCQS> = a(T)|CQS> + b(T)|TQS> with |a(T)|^2 + |b(T)|^2 = 1. If the DNA structure is sufficiently protected to avoid environment-induced decoherence of the confined-proton quantum states, quantum information can be successfully encoded in several base pairs along the coiled double strand. As a potential applicability, a crystalline DNA device could be employed for quantum computing and cryptography controlled by a sequence of Ramsey pulses. Finally, this study critically evaluates these possibilities toward a proof-of-concept of a DNA-based quantum computer.