[RTO × Human-in-Command] Research White Paper on Absolute Control and Quantum Deterministic Optimization Architecture in Autonomous Systems (【RTO × Human-in-Command】自律システムにおける絶対的統制と量子決定論的最適化アーキテクチャの研究白書)
Year: 2026
Type: preprint
Abstract: 【RTO × Human-in-Command】自律システムにおける絶対的統制と量子決定論的最適化アーキテクチャの研究白書 Based on the proprietary theoretical framework of the "Yoko Hasebe Comprehensive Research & Strategic System Integrated White Paper (Master)," this monograph defines a next-generation deterministic architecture designed to transcend the fundamental limitations of existing information engineering paradigms. Addressing the critical vulnerabilities of probabilistic inference uncertainty in current AI systems (deep learning and reinforcement learning) and the cognitive subjugation (rubber-stamping) of human operators under conventional Human-in-the-Loop (HITL) frameworks, this white paper establishes a rigorous engineering, cryptographic, and legal specification for: 1. Human-in-Command (HIC) & Zero-Latency Real-Time Optimization (RTO): Replacing stochastic gradient approximations with physical ground-state convergence via Ising Machine Hamiltonians H(s) = -\sum J_{ij} s_i s_j - \sum h_i s_i for Quadratic Unconstrained Binary Optimization (QUBO) and Model Predictive Control (MPC), enforcing the absolute separation principle: "Intent belongs to humans; computation belongs to the system." 2. Three-Tier Deterministic Defense Architecture (SBC × OCL × QSAFA): Formalizing outer-hull System Boundary Control (SBC) compiled via mathematical Object Constraint Language (OCL) invariants, coupled with a Quantum Secure Automated Foreclosure Architecture (QSAFA) hardware kill-switch utilizing solid-state spin switching and shuttle-based spin-qubit magnetometry to physically sever data buses independent of OS/software layers. 3. Intellectual Property Defense via Backprop-Burn & zkVM Auditing: Countering unauthorized AI scraping and cognitive exploitation through "Energy Basin Erasure" machine unlearning—leveraging empirical Fisher information matrices and Optimal Brain Surgeon (OBS) closed-form weight compensation—verified cryptographically via zero-knowledge virtual machines (zkVM / zk-SNARKs) generating non-interactive Proofs of Unlearning. 4. Next-Generation Governance & Japanese-Style Amnesty Jurisprudence: Mandating QSAFA hardware kill-switches and OCL state-transition proofs under ISO/IEC 42001 Annex A (A.3.2, A.6.2, A.9.4) and EU AI Act Article 14, combined with a strategic safe-harbor amnesty doctrine trading verifiable zkVM unlearning compliance against severe injunctive relief and punitive damages.
Author: Hasebe(長谷部), Yoko(洋子)
Language: English
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225576490