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.
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.
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.
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.
Quantum and DNA computing are distributing and parallel types of computing. They are useful for solving problems which require high complexity computations and (or) massive data set computations such as searching, sorting, merging, pattern recognition, image processing, encryption, etc. Quantum and DNA algorithms cannot be efficiently simulated on classical computers because classical computers cannot efficiently deal with the parallelism. The quantum circuit model is adequate to describe quantum algorithms whereas DNA circuit model is adequate to describe DNA algorithms. This thesis establishes the relationship between a quantum qubit and a DNA string using the Bloch sphere. The Bloch sphere is a convenient graphical representation of a qubit in a 3-dimensional space. The model used in this thesis presents one-to-one mapping between qubits, DNA strings, and points on the Bloch sphere. The model is implemented at the real computer - von Neumann machine, therefore, there are restrictions related to the precision of qubits value, the length of DNA strings, and the precisions of coordinates values. The Bloch sphere and the relations between qubits, DNA strings and points are implemented as "Quantum and DNA Computation Simulation Programming System". Quantum and DNA Computation Simulation Programming System is implemented in the Java programming language. This system is a modified and upgraded interactive Quantum Computation applet developed at the Johns Hopkins Center for Educational Resources. The modifications include connecting (a) DNA strings and the Bloch sphere and (b) DNA strings and qubits. There are many researches in Quantum Computing and DNA Computing as independent study fields, but this is the first research which tries to connect Quantum and DNA Computing together using the Bloch sphere.
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.
This paper introduces “Quantum DNA Swarms,” a unified framework blending quantum-inspired swarm intelligence with DNA’s quantum properties for optimization and self-assembly. Building on Bloch Quantum Ant Colony Optimization (BQACO) [Li & Wang, 2012], we extend Bloch-sphere encoded agents to n-dimensional hypercube spaces via Pauli rotations. The framework integrates DTN bundles for resilient communication, SINUS-IT/QAES for noisy quantum-bio signals, BIONETS autonomic paradigms, experimental DNA-triggered active gels, QI-MARL for energy-aware coordination, GA-GAN for quantum-resistant encryption, and HCDSO with MD-LSTM for malicious detection and QoS-aware multicast routing in MANET environments. This work extends our 2025 Zenodo paper on quantum biological systems and opens new paths for bioinformatics, nano-computing, and space/molecular networks.
Quantum effects in biological systems have long been debated, and DNA represents a particularly intriguing candidate for quantum information processing. Here, we report the first experimental validation of DNA quantum entanglement protocols achieving unprecedented fidelity levels using the Holographic Tree of Life (HoloToL) framework. Our approach combines holographic information encoding, consciousness field dynamics, and quantum error correction to demonstrate DNA base pair entanglement with average quantum fidelity of 99.7% ± 0.3%, far exceeding the theoretical threshold for practical quantum computing applications. We processed 50 synthetic DNA sequences through 200 training epochs, achieving a 100% success rate in the validation of the entanglement protocol with 25/25 sequence pairs maintaining quantum coherence. The framework incorporates bioethical safeguards through topologically protected consciousness fields, ensuring perfect ethical compliance throughout genetic information processing. Our results provide direct evidence for quantum entanglement in DNA Watson-Crick base pairing, validate the existence of decoherence-free subspaces in biological systems, and establish DNA as a viable platform for quantum information storage with biological fidelity exceeding current synthetic quantum computers. These findings bridge quantum physics and molecular biology, opening new paradigms for quantum biotechnology, personalized medicine, and consciousness-integrated artificial intelligence systems.