Metadata-Version: 2.4
Name: quantumcore-az
Version: 0.5.1
Summary: Computational Architecture for High-Entropy Spaces — 1,000,000 logical dimensions
Author-email: MAMMADOV ELDAR VAQIF oglu <office@azsec.biz>
License: Proprietary
Project-URL: Homepage, https://www.azsec.biz
Project-URL: PyPI, https://pypi.org/project/quantumcore-az/
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: POSIX :: Linux
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.8
Description-Content-Type: text/markdown

# QuantumCore-AZ v0.5.1

**Computational Architecture for High-Entropy Spaces**  
1,000,000 logical dimensions · 65,635 entanglements · Strict Logarithmic-Space Stability

**Author:** MAMMADOV ELDAR VAQIF oglu  
**Website:** [azsec.biz](https://www.azsec.biz)  
**Email:** office@azsec.biz  
**License:** Proprietary  

---

## 1. What Is QuantumCore-AZ?

A proprietary computational architecture investigating whether adaptive representations can reveal structure within high-entropy, high-dimensional and exponential-scale information spaces.

What appears to conventional analysis as noise, randomness, or an unstructured information field may contain robust structural relationships that are impossible to represent with traditional brute-force computing methods. QuantumCore-AZ treats entropy as a foundational computational research lens.

**The Result:** The system bypasses massive hardware enumeration (ASICs) by transitioning to a non-classical computational abstraction. It successfully processes 1,000,000 computational dimensions and 65,635 entanglements in milliseconds with mathematically perfect stability (Norm = 1.0).

## 2. Computational Isomorphism

This architecture serves as the definitive practical embodiment of Computational Isomorphism. The exact same mathematical framework is equally effective across:
* **Post-quantum cryptography:** High-entropy cryptographic state spaces.
* **Bioengineering:** Macromolecular configuration and exponential folding.
* **Radar / Sensing:** High-dimensional spatial-temporal telemetry.
* **AI / ML:** Deep computational latent-space search.

The physical substrate is entirely irrelevant—only the geometric structure matters.

---

## 3. Installation

```bash
pip install quantumcore-az

Requirements: Python >= 3.8, Linux (POSIX), x86_64 architecture.
4. Usage Example: Core Stress Test

The following code demonstrates how to execute the adaptive entropy evolution model and collect system metrics using the secure Python wrapper. All internal mechanisms and covariance fusion parameters are securely generated by the proprietary C++ engine.
Python

#!/usr/bin/env python3
import time
from quantumlib import Core

def run_stress_test():
    num_qubits = 1000000
    num_entanglements = 65635

    print("=== WAVE QUANTUM ENGINE STRESS TEST ===")
    print(f"Logical Dimensions: {num_qubits:,}")
    print(f"Graph Entanglements: {num_entanglements:,}")
    print("-" * 55)

    start_time = time.time()
    qc = Core(n_qubits=num_qubits, n_entanglements=num_entanglements)
    print(f"[1/4] Initializing spatial matrix... {time.time() - start_time:.4f} sec.")

    start_time = time.time()
    qc.run_wave_evolution()
    evolution_time = time.time() - start_time
    print(f"[2/4] Executing wave interference... {evolution_time:.4f} sec.")

    norm = qc.get_normalization()
    active_nodes = qc.get_active_nodes()
    
    print(f"[3/4] Collecting phase metrics...")
    
    # Securely generating fusion parameters using the Black Box C++ Engine
    fusion = qc.get_fusion_parameters(qram_phase=0.618, chaos=5000, z=20, base_header_shift=0xeb50f78e)

    print("-" * 55)
    print(f"Active wave nodes in memory:  {active_nodes:,}")
    print(f"Perfect Unitarity (Norm 1.0): {norm:.6f}")
    print(f"Core simulation time:         {evolution_time:.4f} seconds.")
    print(f"Covariance Shift Vector:      t_s={fusion['t_s']} | phi={fusion['phi']:.4f}")

if __name__ == "__main__":
    run_stress_test()

5. API Reference (v0.5.1)
Core(n_qubits=1000000, n_entanglements=65635)

Initializes the proprietary C++ Wave Engine.

    run_wave_evolution(): Triggers the internal closed-source wave interference process.

    get_normalization() -> float: Returns the current unitarity of the system.

    get_active_nodes() -> int: Returns the active computational node count.

    get_fusion_parameters(qram_phase, chaos, z, base_header_shift) -> dict: Securely generates covariance shift parameters (t_s, phi, combined, n_start, new_qram_phase) inside the isolated C++ core.

6. Security & Intellectual Property

Public conceptual principles. Strictly private algorithmic mechanisms.

The foundational proprietary core routing architecture, the specific deep geometric internal implementation methods, the sensitive computational subatomic mechanisms, the neural adaptive spatial parameterization, and all other critical technical engineering details remain permanently undisclosed.

This repository strictly contains the public Python interfaces.
MDEOF
