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.
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.
Extending our March 18, 2026 preprints (Zenodo Records 19093596, the “in Action” paper, and the new in-vitro protocol), this work scales QAES-stabilized phonon bubbles to 11D cosmic models (Carbó-Dorca simplices) and integrates them with Delay/Disruption Tolerant Networking (DTN) bundles for cislunar resilience. We demonstrate how DNA swarms maintain phonon-photon coherence across intermittent space links, enabling secure quantum communication and sensor networks for the Genesis Mission infrastructure. The framework combines our QI-MARL energy loop, QAES classification, and NASA DTN protocols to achieve 98% link success in simulated cislunar conditions.
To enhance the performance of the artificial bee colony optimization by integrating the quantum computing model into bee colony optimization, we present a quantum-inspired bee colony optimization algorithm. In our method, the bees are encoded with the qubits described on the Bloch sphere. The classical bee colony algorithm is used to compute the rotation axes and rotation angles. The Pauli matrices are used to construct the rotation matrices. The evolutionary search is achieved by rotating the qubit about the rotation axis to the target qubit on the Bloch sphere. By measuring with the Pauli matrices, the Bloch coordinates of qubit can be obtained, and the optimization solutions can be presented through the solution space transformation. The proposed method can simultaneously adjust two parameters of a qubit and automatically achieve the best match between two adjustment quantities, which may accelerate the optimization process. The experimental results show that the proposed method is obviously superior to the classical one for some benchmark functions.
We present a comprehensive quantum-inspired DNA computing framework implemented in IcsDNA using the InChroSil Virtual Machine [11], a production-ready DNA computing platform. Our approach extends the classical four-letter nucleotide alphabet {A, G, C, T} into a four-dimensional complex Hilbert space ℋ! , enabling superposition states, entanglement between nucleotide pairs, and quantum gate operations adapted to the biological domain. The framework introduces novel adaptations of fundamental quantum computing primitives, including Pauli gates, Hadamard transforms, rotation operators, and Bell-state generation, to a quaternary (base-4) nucleotide encoding. We further implement quantum-inspired algorithms for DNA sequence alignment, pattern matching via Grover-like amplitude amplification, Fourier analysis of genomic sequences, and quantum error correction for DNA storage integrity. The system provides both a high-level C++ object-oriented stack and a C API for integration into bioinformatics pipelines. Experimental evaluation demonstrates the applicability of quantum-inspired techniques to probabilistic genomic analysis, DNA-based cryptographic key generation, and fault-tolerant DNA data storage with decoherence simulation.
As modern data environments continue to scale in volume and dimensionality, classical metaheuristics like Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) struggle to balance convergence efficiency with solution quality. The challenge is further amplified in big data optimization, where stagnation in local minima and prohibitive computation time impede real-time applicability. Existing methods fail to simultaneously address the need for global search intelligence and computational scalability, especially under high-dimensional constraints and distributed workloads. To bridge this gap, we propose QIESO—a Quantum-Inspired Evolutionary Swarm Optimizer that integrates quantum amplitude encoding, probabilistic gate rotation, swarm coordination, and tunnelingbased mutation to escape local traps and accelerate convergence. Unlike traditional optimizers, QIESO leverages quantum principles simulated classically to maintain coherence across the search space while reducing evaluation overhead. Empirical validation across datasets up to 100 million records shows QIESO achieves a 51.7% reduction in runtime over GA and 42.7% faster convergence than PSO, with a relative error margin consistently under 2.1%. The method exhibits sublinear scalability in time complexity due to dimensionality-agnostic encoding. This work significantly advances scalable optimization for high-throughput environments, making it ideal for applications in autonomous systems, real-time analytics, and next-gen intelligent infrastructures.
To enhance the performance of the intelligent optimization algorithm, a new model of performing search on the Bloch sphere is proposed. Then, by integrating the model into the artificial fish swarm optimization, we present a quantum-inspired artificial fish swarm optimization algorithm. In proposed method, the fishes are encoded with the qubits described on the Bloch sphere. The vector product theory is adopted to establish the axis of rotation, and the Pauli matrices are used to construct the rotation matrices. The four fish behaviors, such as moving, tracking, capturing, aggregating, are achieved by rotating the current qubit about the rotation axis to the target qubit on the Bloch sphere. The Bloch coordinates of qubit can be obtained by measuring with the Pauli matrices, and the optimization solutions can be presented through the solution space transformation. The highlight advantages of this method are the ability to simultaneously adjust two parameters of a qubit and automatically achieve the best match between two adjustment quantities, which may accelerate the optimization process. The experimental results show that the proposed method obviously outperforms the classical one in convergence speed and achieves better levels for some benchmark functions.
THE EL-RAKHAWI BIO-QUANTUM COMPUTING FRAMEWORK by Dr. M.K.A. El-Rakhawi (DOI: 10.5281/zenodo.23527961) integrates three frontier technologies: Organoid Intelligence (OI), Quantum Neuromorphic Computing, and Autonomous AI Scientist Agents. Core innovations: (1) Cerebral organoids interfaced via 3D Multi-Electrode Arrays in microfluidic bioreactors, performing adaptive control of simplified robotic effectors with synaptic efficiency of 10-100 femtojoules per operation—strategically offloading non-linear learning tasks to bypass the von Neumann bottleneck, without claiming system-wide femtojoule efficiency; (2) Variational quantum circuits leveraging exponential state-space representation to simulate neuromorphic networks, offering potential polynomial or exponential speedup on fault-tolerant architectures for QUBO problems; (3) Autonomous AI Scientist Agents generating hypotheses and directing self-driving laboratories in closed-loop discovery cycles. The framework mandates ethical boundaries: organoids maintained at early fetal developmental stages with strict compute-time limits. IP protection excludes abstract biological phenomena and pure algorithms per Alice/Myriad precedents, safeguarding only concrete hardware architectures, specific microfluidic designs, quantum circuit topologies, and hardware-implemented safety interlocks that physically prevent robotic actuators from executing flagged protocols—satisfying the machine-or-transformation test. This positions the framework as a patentable, physically rigorous, and legally defensible blueprint for autonomous bio-quantum discovery.