Quantum AI JML Visualizer

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

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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.

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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

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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

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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.

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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)
);

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Drift Analysis (Snippet)

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

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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];

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JML Dashboard (Snippet)

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

The kernel converts this JML into HTML automatically.

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License

This project is licensed under the AGL-F License.

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Author

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