https://github.com/mohamedsakeel/qiajs-algorithm

Explore QIAJS, a novel search algorithm leveraging quantum inspiration, machine learning, and hybrid data structures for potential speed breakthroughs. This repository welcomes collaboration on testing and refining its innovative approach! ✨
https://github.com/mohamedsakeel/qiajs-algorithm

Last synced: 2 months ago
JSON representation

Explore QIAJS, a novel search algorithm leveraging quantum inspiration, machine learning, and hybrid data structures for potential speed breakthroughs. This repository welcomes collaboration on testing and refining its innovative approach! ✨

• Host: GitHub
• URL: https://github.com/mohamedsakeel/qiajs-algorithm
• Owner: mohamedsakeel
• License: gpl-3.0
• Created: 2024-01-20T16:18:24.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-01-20T16:23:46.000Z (over 1 year ago)
• Last Synced: 2025-01-09T11:38:31.149Z (4 months ago)
• Size: 17.6 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Quantum-Inspired Adaptive Jump Search (QIAJS)

**Exploring a Novel Algorithm for Enhanced Search Speed and Efficiency**

## Overview

QIAJS is a conceptual search algorithm that blends quantum-inspired principles, machine learning, and hybrid data structures to potentially outperform traditional algorithms. This repository invites collaboration for testing, refining, and advancing its innovative approach.

**Key Features:**

- **Quantum-Inspired Probing:** Explores multiple potential target areas simultaneously, guided by quantum probability calculations.

- **Machine Learning Adaptation:** Dynamically adjusts jump sizes and probabilities based on search patterns and data characteristics.

- **Hybrid Data Structure:** Combines BST and hash table for efficient access and updates.

## Usage

**Installation:**

1. Clone the repository: `git clone https://github.com/mohamedsakeel/QIAJS.git`

2. Install dependencies (listed in `requirements.txt`)

**Code Examples:**

```python

# Example usage (Python)

import qiajs

data = [10, 4, 25, 7, 15]  # Sample data

target = 7

index = qiajs.search(data, target)

if index is not None:

    print("Target found at index:", index)

else:

    print("Target not found")