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Quantum AI JML Visualizer

Visualization of Quantum Decoherence & AI Drift Prediction using Ascoos OS Kernel 1.0.0

License PHP Ascoos OS Category


Overview

This repository contains the quantum-ai-jml-visualizer case study for Ascoos OS Kernel 1.0.0.

It demonstrates how the kernel performs:

  • Quantum simulation (Bell State |Φ+>, decoherence, Everett branching)
  • Statistical analysis (variance-based drift factor)
  • Neural network prediction (instability detection)
  • JML-based UI rendering (native, zero-dependency HTML generation)

Everything runs natively, without frameworks, without template engines, and without external libraries.


Features

  • Quantum state normalization & unitary evolution
  • Decoherence simulation using λ-parameter
  • Z-basis measurement with branching probabilities
  • Variance-based drift analysis
  • Neural network training & prediction (ReLU + Sigmoid)
  • JML dashboard rendering (dark mode, responsive grid)
  • Zero dependencies — powered entirely by Ascoos OS Kernel

File Structure

/quantum-ai-jml-visualizer
│
├── quantum_ai_jml_visualizer.php   # Main case study file
├── LICENSE.md                      # AGL-F License
├── README.md                       # English documentation
└── README-GR.md                    # Greek documentation

Running the Case Study

Requires:

  • PHP 8.4+
  • Ascoos OS Kernel 1.0.0

Run:

php quantum_ai_jml_visualizer.php

The script outputs a fully rendered HTML dashboard generated from JML.


Quantum Logic (Snippet)

$bellState = $quantum->normalize([
    [0.707, 0.0], [0.0, 0.0],
    [0.0, 0.0],   [0.707, 0.0]
]);

$lambda = 0.75;
$U = $math->tensor($I, $D);
$noisyState = $quantum->normalize(
    $quantum->applyUnitary($U, $bellState)
);

Drift Analysis (Snippet)

$driftFactor = (new TStatisticAnalysisHandler([
    $branchesZ[0]['probability'],
    $branchesZ[1]['probability']
]))->variance();

AI Prediction (Snippet)

$ai->compile([
    ['input'=>1,'output'=>4,'activation'=>'relu'],
    ['input'=>4,'output'=>1,'activation'=>'sigmoid']
]);

$ai->fit([[$driftFactor]], [($driftFactor > 0.2 ? 1 : 0)], epochs:100);
$prediction = $ai->predictNetwork([[$driftFactor]])[0];

JML Dashboard (Snippet)

div:class('status-bar'),style('background:{$statusColor}') {
    `STATUS: {$statusText}`
}

The kernel converts this JML into HTML automatically.


License

This project is licensed under the AGL-F License.


Author

Drogidis Christos
Creator of Ascoos OS
https://www.ascoos.com

About

Visualization of Quantum Decoherence and AI Decision through JML.

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