QuantumCitizen ⚛🌍

🚀 Overview

QuantumCitizen is an educational software developed in Ruby that explores the integration of quantum random number generation and data extraction from a Human Development Index (HDI) dataset. This project combines the power of modern APIs with CSV data manipulation, enabling the analysis of specific indicators for various countries selected based on a quantum-generated value.

🌟 Features

• ⚛️ Quantum Number Generation: Integrates the quantum random number generator API by the Australian National University (ANU).
• 📊 Dataset Analysis: Works with an HDI CSV dataset containing data from 1990 to 2021.
• 🧠 Quantum-Based Selection:
  • The software uses quantum-generated values to select a country from the dataset.
  • It calculates the "best available year" based on data completeness for the selected country.
• 🧩 Clear Interface: Provides a logical flow for querying, selection, and displaying relevant results.

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🧰 Technologies & Libraries

Languages and Tools

• Ruby: Version 3.2.0
• CSV: Standard library for handling CSV files.

Integrations

• ANU Quantum API: Used to generate truly random quantum values based on vacuum quantum fluctuations.

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🧪 How It Works

1. Quantum Generation:
   • The program queries the ANU API to obtain a random quantum number.
2. Country Selection:
   • The quantum number is used as an index to select a country from the dataset (modulo the total number of countries).
3. Best Year Determination:
   • The software evaluates available HDI data for the selected country and picks the most complete year.
4. Data Extraction and Visualization:
   • Displays the HDI and other key indicators for the selected country and year.

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📖 Setup Guide

Prerequisites

• Ruby (Version 3.2.0 or later): Install Ruby
• Git: To clone the repository.
• HDI CSV dataset (already included in the repository).

Installation

1. Clone the Repository

   git clone https://github.com/tuusuario/QuantumCitizen.git
   cd QuantumCitizen

2. Obtain API key and SSL certificate
   You need to create an account on the official site of ANU and generate your key https://quantumnumbers.anu.edu.au
   Download a fresh SSL certificate from https://curl.se/ca/cacert.pem
   

3. Set Up Environment Variables Create an .env file in the root directoy with the following content:

ANU_API_KEY=your_private_key
SSL_CERT_FILE_PATH=C:/your/local/route/cacert.pem

4. Run the software

ruby main.rb

Project Structure

    QuantumCitizen/
  │
  ├── dataset/
  │   └── HDI.csv              # HDI dataset (1990-2021)
  │
  ├── lib/
  │   ├── country_data.rb      # Module for dataset manipulation
  │   ├── quantum_generator.rb # Module for quantum API integration
  │   └── visualizer.rb        # Optional module for future visualizations
  │
  ├── spec/                    # Tests (in development)
  ├── .env                     # Environment variables
  ├── main.rb                  # Main program entry point
  ├── Gemfile                  # Dependencies (no external gems in this project)
  ├── LICENSE                  # BSD-3 License
  └── README.md                # Project documentation

📊 Dataset

The HDI.csv file contains human development data from 1990 to 2021, including:

• ISO3: Country code.
• Country: Country name.
• HDI Rank (2021): Human Development Index rank for 2021.
• Human Development Index (by year): Human Development Index values.
• Life Expectancy at Birth (by year): Life expectancy at birth.

⚗️ How to Use

1. Run the program with rub main.rb
2. The software will generate a quantum number
3. Using the number, it will select a country from the dataset
4. It will identify the best available year and display the HDI and other key indicators

⚖️ License

This project is licensed under the tems of the BSD-3 Clause License

See the LICENSE file for more details

🔮 Contributing

Contibutions are welcome! Follow these steps:

1. Fork the Project

2. Create a feature branch:

git checkout -b feature/new-feature

3. Make your changes and commit them

4. Open a Pull Request!

🎯 Future implementations

• Data visualization: Add graphs using Plotly or similar libraries

• New Dataset Integrations: Expand the analysis to other HDI metrics