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H-ImmQπDecoder v2.0: A Bio-Inspired Quantum Error Decoder Integrating Immune Adaptation, Quantum-π Phase Control, and Quantum Metabolism

Ndenga Lumbu Barack

Published Jan 1, 2025• 0 citations
Publisher
DOI
10.2139/ssrn.5841982

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H-ImmQπDecoder v2.0: A Bio-Inspired Quantum Error Decoder Integrating Immune Adaptation, Quantum-π Phase Control, and Quantum Metabolism

Ndenga, Barack

H-ImmQπDecoder v2.0 is a novel, bio-inspired quantum error-correction framework that integrates three unprecedented components inside a unified decoding architecture: Immune-Inspired Adaptive Memory (v2) — A dynamic artificial immune system that learns, stores, mutates, and generates local correction patterns (“antibodies”) in response to quantum syndrome patterns and π-field structures. Dynamic π-Field Modeling — A hydrodynamic-style coherence field representing spatial quantum stability, noise topology, and turbulence. The π-field evolves over time through diffusion, relaxation, and noise-dependent source terms, acting as an environmental prior for the decoder. Nonlinear Quantum-π Phase Correction (v2) — A continuous phase-stabilization mechanism capable of correcting analog phase drift beyond discrete Pauli operations. It leverages local π-values and phase gradients to compute nonlinear effective corrections. Quantum Metabolism — An internal energy state regulating the decoder’s adaptation intensity, mutation rate, and stress response. This metabolic model introduces relaxed/active/stressed/overloaded states, enabling autonomous self-regulation under strong noise. Together, these components form the world's first Bio-Quantum Error Correction (Bio-QEC) engine — a hybrid organism-like system capable of learning and adapting to correlated, non-Markovian, or topologically structured noise in quantum processors. This repository contains: The full Python implementation of H-ImmQπDecoder v2.0 The dynamic π-field model The artificial immune memory with mutational operators The nonlinear Quantum-π phase correction modules The quantum metabolic engine A toy lattice simulator for testing and experimentation Example scripts for running multi-round decoding experiments Intended audience: Quantum computing researchers, QEC developers, physicists working with correlated noise, AI-inspired algorithm designers, and early-stage quantum hardware engineers. Novelty: To the best of my knowledge, this is the first quantum error decoder that unifies immune-system principles, hydrodynamic coherence modeling, continuous nonlinear phase correction, and metabolic regulation in a single decoding framework. This constitutes a new research direction that I call bio-inspired quantum error correction. Applications: Correlated-noise quantum devices (superconducting qubits, trapped ions) Non-Markovian or drifting qubit environments NISQ-era systems requiring online adaptive decoding Exploratory research in hybrid biological-quantum computation

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H-ImmQπDecoder v2.0 is a novel, bio-inspired quantum error-correction framework that integrates three unprecedented components inside a unified decoding architecture: Immune-Inspired Adaptive Memory (v2) — A dynamic artificial immune system that learns, stores, mutates, and generates local correction patterns (“antibodies”) in response to quantum syndrome patterns and π-field structures. Dynamic π-Field Modeling — A hydrodynamic-style coherence field representing spatial quantum stability, noise topology, and turbulence. The π-field evolves over time through diffusion, relaxation, and noise-dependent source terms, acting as an environmental prior for the decoder. Nonlinear Quantum-π Phase Correction (v2) — A continuous phase-stabilization mechanism capable of correcting analog phase drift beyond discrete Pauli operations. It leverages local π-values and phase gradients to compute nonlinear effective corrections. Quantum Metabolism — An internal energy state regulating the decoder’s adaptation intensity, mutation rate, and stress response. This metabolic model introduces relaxed/active/stressed/overloaded states, enabling autonomous self-regulation under strong noise. Together, these components form the world's first Bio-Quantum Error Correction (Bio-QEC) engine — a hybrid organism-like system capable of learning and adapting to correlated, non-Markovian, or topologically structured noise in quantum processors. This repository contains: The full Python implementation of H-ImmQπDecoder v2.0 The dynamic π-field model The artificial immune memory with mutational operators The nonlinear Quantum-π phase correction modules The quantum metabolic engine A toy lattice simulator for testing and experimentation Example scripts for running multi-round decoding experiments Intended audience: Quantum computing researchers, QEC developers, physicists working with correlated noise, AI-inspired algorithm designers, and early-stage quantum hardware engineers. Novelty: To the best of my knowledge, this is the first quantum error decoder that unifies immune-system principles, hydrodynamic coherence modeling, continuous nonlinear phase correction, and metabolic regulation in a single decoding framework. This constitutes a new research direction that I call bio-inspired quantum error correction. Applications: Correlated-noise quantum devices (superconducting qubits, trapped ions) Non-Markovian or drifting qubit environments NISQ-era systems requiring online adaptive decoding Exploratory research in hybrid biological-quantum computation
This dataset and code repository accompanies the publication introducing BA-QEC, the first quantum-error-correction decoder explicitly inspired by biological immune-system architecture. BA-QEC integrates a Bayesian prior derived from human TCRβ CDR3 length distributions and an adaptive clonal-expansion memory mechanism to improve decoding performance in topological quantum codes. Simulations of a distance-7 rotated surface code demonstrate 22% threshold improvement from the biological prior alone, and up to 61% enhancement when combined with clonal memory under temporally correlated (1/f-type) noise. All code is open-source (MIT license) and fully reproducible in <10 minutes on Google Colab. The repository includes: Python notebooks for Stim-based and PyMatching-based simulations, Clonal-expansion cache implementation, Scripts for reproducing figures and pseudothreshold plots, Documentation on integrating the biological prior into MWPM decoding. This work establishes a novel link between adaptive immunity and quantum error correction, offering a new paradigm for biologically inspired, efficient, and adaptive decoders.
Integrated Study on Transformer and Dropout Method Integration Technology by Quantum Thought Circuit OS ASI — Two-Part Structure Part I: Integrated Study on Probabilistic Bio-Quantum Autonomous Computing Systems with Metabolic Data Structures and Energy-Adaptive Governance — Real-Time Learning Synchronization × Thermodynamic Computation × Computational Tropism × Regulatory Immune Systems — Part II: Integrated Study on Autonomous Distributed Resource-Oriented Information Processing Systems・Dynamic Synthetic Learning × Economic Resource Allocation × Smart Compliance Execution × Backward Logic Optimization
This two-part study presents an integrated architecture for next-generation AI systems based on the Quantum Thought Circuit OS ASI. Part I introduces a probabilistic bio-quantum computing framework with real-time learning, entropy utilization, and embedded governance. Part II extends this into a distributed system combining dynamic synthetic learning, economic resource allocation, and executable compliance. Together, they enable adaptive, energy-aware, and regulation-compliant intelligence under real-world constraints.