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

python

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)

S-Rank-Hunter/Alpha_X8_SMILE_SEQ

Q-VEDHA (ALPHA X8-MAX)