S-Rank-Hunter/Alpha_X8_SMILE_SEQ

Q-VEDHA (ALPHA X8-MAX)

Quantum-Validated Engine for Drug Discovery & Holistic Analysis

Q-VEDHA (ALPHA X8-MAX) is a sovereign quantum-hybrid Large Reasoning Model (LRM) for autonomous de-novo molecular generation. It combines High-Dimensional Reinforcement Learning (HDRL) with quantum-mechanical simulations to explore chemical space beyond classical limits, operating at photonic inference speeds with strict sovereign safety guarantees.

---

Model Summary

• Type: Hybrid Quantum-Classical Large Reasoning Model
• Primary Task: De-novo drug discovery and autonomous synthesis planning
• Core Engine: Photonic-MAXIMUS Core (~45B parameters)
• Quantum Stack: ADAPT-VQE, QGAN, QRL, Deutsch–Jozsa Logic
• Validation Metric: τ (Tau) ≥ 0.85
• License: CreativeML Open RAIL-M

---

Key Capabilities

• Quantum-validated protein–ligand interaction modeling
• Reinforcement-driven molecular generation
• Noise-driven discovery using quantum decoherence
• Autonomous synthesis protocol generation (flow chemistry)
• Full data and IP sovereignty (no external APIs)

---

Architecture Overview

Component	Description
Logic Core	Sovereign Large Reasoning Model
Learning	High-Dimensional Reinforcement Learning
Optimization	Quantum Natural Gradient
Inference	< 5 ms (photonic path)
Quantum Target	127-qubit gate-based processor

---

Autonomous Synthesis

Q-VEDHA generates Autonomous Synthesis Protocols (ASP) compatible with microfluidic, no-robot flow-chemistry systems, enabling closed-loop discovery-to-synthesis pipelines with in-line sensor feedback.

---

Evaluation Metrics

Metric	Description
τ (Tau)	Sovereign safety and confidence score
Binding Affinity	kcal/mol (quantum-validated)
Synthetic Accessibility	Chemical feasibility

Only candidates with τ ≥ 0.85 qualify for synthesis.

---

Usage Example

import q_vedha

model = q_vedha.load("alpha-x8-max")
manifold = model.init_manifold(omics_data="patient_001.json")

candidate = model.generate(
    target_protein="6VSB",
    constraints={"toxicity": "low"}
)

if candidate.metrics.tau >= 0.85:
    model.lab.trigger_synthesis(candidate.protocol)