Photonic Attractor Network (PAN) and Q-SAFA Governance Integration Module: Deterministic <12.5 ms HITLO Core, ML-KEM-768 Quantum Audit Records, and Step-Function Risk Rejection for Global PHR, Smart Kitchen, and 100-Yen Smart Bowl DePIN Infrastructur

This repository and monograph establish the formal verification architecture and reference simulation implementation for the Photonic Attractor Network (PAN) & Q-SAFA Governance Integration Module, designed for edge deployment across Global Personal Health Record (PHR) networks, Smart Kitchen environments, and the 100-Yen Smart Bowl decentralized physical infrastructure network (DePIN).Conventional IoT health and kitchen appliances suffer from two systemic vulnerabilities: (1) uncompensated cloud-routing latency that fails to halt physical thermal runaways in real time, and (2) unauthorized harvesting and cryptographic exposure of sensitive biometric/nutritional telemetry. To resolve these failures deterministically at the hardware edge (HRoT / FPGA), this architecture integrates three core mechanisms:Ultra-Low Latency HITLO Core ($<12.5\text{ ms}$ Deterministic Execution):The Hard-In-The-Loop Optimization (SmartKitchenHITLOCore) engine processes real-time thermal sensors (temperature_c), smoke density (smoke_ppm), and 100-yen smart bowl nutritional telemetry (calories_kcal, protein_g, flaxseed_oil_mg, maitake_mushrooms_g, natto_g) within a strict hardware latency ceiling of $\tau_{\text{budget}} = 12.5\text{ ms}$, ensuring immediate physical safety assertion (EMERGENCY_SHUTOFF_ASSERTED / SAFETY_HALT) without cloud dependency.Quantum Governance & Data Sovereignty Engine (ML-KEM-768 & Step-Function Risk Rejection):Operating inside a Sealed TEE Realm (SEALED_TEE_REALM), the QuantumGovernanceEngine enforces FIPS 203 post-quantum key encapsulation (ML-KEM-768, public key size $1,184\text{ bytes}$, ciphertext size $1,088\text{ bytes}$) via NTT-accelerated FPGA hardware ($\sim 0.035\text{ ms}$ latency) and records an immutable Quantum Audit Record (QAR) for every sensor cycle. Crucially, the engine replaces gameable linear risk trade-offs with a strict Step-Function Risk Rejection constraint over the secondary hazard risk score:$$f_3(T, S) = 0.01 + \frac{\max(0, T - 180.0)}{200.0} + \frac{\max(0, S - 50.0)}{1000.0}$$Whenever $f_3 > \theta_{\text{ethical}} = 0.05$ ($5.0\%$), the governance functional assigns an immediate infinite penalty ($P(f_3) = +\infty$), halting physical actuation and blocking token settlement.DePIN Health Incentive & Multi-Objective Optimization (MOO) Grid:The DePINHealthIncentiveGrid evaluates four Pareto objectives—maximizing the Health Index Score ($f_1$), minimizing resource cost ($f_2$), enforcing the secondary hazard step-constraint ($f_3 \le 0.05$), and maximizing DePIN token return ($f_4$). Nutritional quality is evaluated as:$$H_{\text{score}} = \text{clip}_{[0, 100]}\!\left(1.2 P_{\text{protein}} + \frac{M_{\text{flaxseed}}}{200.0} - 0.05 \left\vert{}C_{\text{kcal}} - 600\right\vert{}\right)$$Under verified safe operation ($f_3 \le 0.05$), the module dispatches DePIN health reward tokens ($0.5 \times H_{\text{score}}$) and computes a dynamic health insurance discount ($0.15 \times H_{\text{score}}\%$). Under high thermal/smoke hazard conditions ($f_3 > 0.05$), reward distribution is deterministically rejected (REJECTED_DUE_TO_HAZARD).