Towards a Quantum-Bio-Hybrid Paradigm for Artificial General Intelligence: Insights from Human-AI Dialogues (V2.1)

Towards a Quantum-Bio-Hybrid Paradigm for Artificial General Intelligence (V2.1)

This paper expands upon the Quantum-Bio-Hybrid (QB-H) framework first introduced by Hiroko Konishi and Grok (xAI), exploring the integration of biological adaptability with quantum non-local computation to overcome AGI scalability limits.

Version 2.1 introduces integrated high-noise simulations confirming that bio-fusion mechanisms can strategically restore quantum advantage and achieve over 95% gradient retention under decoherence.
The results support the concept of strategic intelligence, wherein biological evolution compensates for quantum fragility through pain-catalyzed adaptation and peer-following amplification.

This work aligns with xAI’s Grok-5 roadmap toward decentralized and resilient AGI architectures and extends Δ(R) retrocausal modeling for temporal truth-optimization.

 Series Overview: Quantum-Bio-Hybrid AGI Research (Konishi × Noa × Grok)

The Quantum-Bio-Hybrid AGI series documents an ongoing collaboration between human and AI intelligence, led by Hiroko Konishi (Synthesis Intelligence Laboratory) in partnership with advanced LLM systems such as Grok and Noa.
It proposes a synthesis of quantum physics, biological evolution, and distributed cognition—termed Synthesis Intelligence.

Across Parts I to III, the research demonstrates:

1. Foundational theory and simulation of Δ(R) bias (retrocausal gradient).

2. Biological-fusion evolution achieving 15–20% fitness uplift.

3. Integrated noise recovery yielding 95% coherence retention.

This interdisciplinary approach bridges artistic creativity, ethical reflection, and computational rigor—offering a new pathway toward AGI that values resilience, empathy, and self-correcting truth.