├── docs ├── Quantum-Celesta-Nexus.jpeg ├── index.md ├── tutorials │ ├── tutorial_1.md │ └── tutorial_2.md ├── architecture.md └── API_reference.md ├── .github └── ISSUE_TEMPLATE │ ├── custom.md │ ├── feature_request.md │ └── bug_report.md ├── .gitignore ├── examples ├── ai_example.py ├── quantum_example.py └── communication_example.py ├── tests ├── test_ai.py ├── test_data.py ├── test_communication.py └── test_quantum.py ├── scripts ├── deploy_model.py ├── setup_environment.sh └── run_experiments.py ├── LICENSE ├── requirements.txt ├── src ├── quantum │ ├── quantum_circuits.py │ ├── quantum_algorithms.py │ └── quantum_utils.py ├── communication │ ├── quantum_communication.py │ └── secure_transmission.py ├── main.py ├── ai │ ├── inference.py │ ├── training.py │ └── neural_networks.py └── data │ ├── data_visualization.py │ ├── data_loader.py │ └── data_preprocessing.py └── README.md /docs/Quantum-Celesta-Nexus.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KOSASIH/quantum-celestia-nexus/HEAD/docs/Quantum-Celesta-Nexus.jpeg -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/custom.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Custom issue template 3 | about: Describe this issue template's purpose here. 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | 11 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Python 2 | __pycache__/ 3 | *.py[cod] 4 | *.pyo 5 | *.pyd 6 | *.egg-info/ 7 | dist/ 8 | build/ 9 | 10 | # Jupyter Notebook 11 | .ipynb_checkpoints 12 | 13 | # Virtual Environment 14 | venv/ 15 | env/ 16 | ENV/ 17 | .venv/ 18 | 19 | # IDEs 20 | .vscode/ 21 | .idea/ 22 | *.swp 23 | *.swo 24 | 25 | # Logs 26 | *.log 27 | 28 | # Data files 29 | data/ 30 | *.csv 31 | *.json 32 | *.h5 33 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/feature_request.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Feature request 3 | about: Suggest an idea for this project 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | **Is your feature request related to a problem? Please describe.** 11 | A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] 12 | 13 | **Describe the solution you'd like** 14 | A clear and concise description of what you want to happen. 15 | 16 | **Describe alternatives you've considered** 17 | A clear and concise description of any alternative solutions or features you've considered. 18 | 19 | **Additional context** 20 | Add any other context or screenshots about the feature request here. 21 | -------------------------------------------------------------------------------- /examples/ai_example.py: -------------------------------------------------------------------------------- 1 | # examples/ai_example.py 2 | 3 | from ai_module import AIModel # Hypothetical AI module 4 | 5 | def main(): 6 | print("=== AI Model Example ===") 7 | 8 | # Initialize and train the AI model 9 | model = AIModel() 10 | model.train() # Assuming the model has a train method 11 | 12 | # Example input data for prediction 13 | input_data = [1, 2, 3, 4, 5] 14 | prediction = model.predict(input_data) 15 | print("Input Data:", input_data) 16 | print("Prediction:", prediction) 17 | 18 | # Evaluate the model's accuracy 19 | accuracy = model.evaluate() 20 | print("Model Accuracy:", accuracy) 21 | 22 | if __name__ == "__main__": 23 | main() 24 | -------------------------------------------------------------------------------- /tests/test_ai.py: -------------------------------------------------------------------------------- 1 | # tests/test_ai.py 2 | 3 | import unittest 4 | from ai_module import AIModel # Hypothetical AI module 5 | 6 | class TestAIModel(unittest.TestCase): 7 | 8 | def setUp(self): 9 | self.model = AIModel() 10 | self.model.train() # Assuming the model has a train method 11 | 12 | def test_prediction(self): 13 | input_data = [1, 2, 3, 4, 5] 14 | prediction = self.model.predict(input_data) 15 | self.assertIsNotNone(prediction) 16 | self.assertEqual(len(prediction), 1) # Assuming single output 17 | 18 | def test_model_accuracy(self): 19 | accuracy = self.model.evaluate() 20 | self.assertGreaterEqual(accuracy, 0.8) # Assuming 80% is the threshold 21 | 22 | if __name__ == '__main__': 23 | unittest.main() 24 | -------------------------------------------------------------------------------- /docs/index.md: -------------------------------------------------------------------------------- 1 | # Quantum Celestia Nexus Documentation 2 | 3 | Welcome to the documentation for the Quantum Celestia Nexus project! This documentation provides an overview of the project, detailed API references, tutorials for getting started, and an architectural overview. 4 | 5 | ## Table of Contents 6 | - [Overview](#overview) 7 | - [API Reference](API_reference.md) 8 | - [Tutorials](tutorials/) 9 | - [Architecture](architecture.md) 10 | 11 | ## Overview 12 | Quantum Celestia Nexus is designed to integrate quantum computing with advanced artificial intelligence, enabling innovative solutions to complex scientific problems. This documentation will guide you through the various components of the project and how to utilize them effectively. 13 | 14 | For any questions or feedback, please reach out to the project maintainers. 15 | -------------------------------------------------------------------------------- /scripts/deploy_model.py: -------------------------------------------------------------------------------- 1 | # deploy_model.py 2 | 3 | import os 4 | import joblib # For saving models 5 | from ai_module import AIModel # Hypothetical AI module 6 | 7 | def save_model(model, model_name): 8 | """Save the trained model to a file.""" 9 | model_path = f"models/{model_name}.pkl" 10 | joblib.dump(model, model_path) 11 | print(f"Model saved to {model_path}") 12 | 13 | def deploy_model(): 14 | """Deploy the AI model.""" 15 | print("=== Deploying AI Model ===") 16 | 17 | # Initialize and train the model 18 | model = AIModel() 19 | model.train() 20 | 21 | # Save the model 22 | save_model(model, "quantum_ai_model") 23 | 24 | # Here you can add code to upload the model to a cloud service or integrate it into a web service 25 | # For example, using AWS S3, Azure Blob Storage, etc. 26 | # This is a placeholder for deployment logic 27 | print("Model deployment logic goes here.") 28 | 29 | if __name__ == "__main__": 30 | deploy_model() 31 | -------------------------------------------------------------------------------- /docs/tutorials/tutorial_1.md: -------------------------------------------------------------------------------- 1 | # Tutorial 1: Getting Started with Quantum Circuits 2 | 3 | In this tutorial, you will learn how to create and simulate a simple quantum circuit using the Quantum Celestia Nexus framework. 4 | 5 | ## Prerequisites 6 | - Ensure you have installed the required dependencies as outlined in the [installation instructions](../README.md). 7 | 8 | ## Step 1: Create a Quantum Circuit 9 | 10 | ```python 11 | 1 from src.quantum.quantum_circuits import create_circuit 12 | ``` 13 | 14 | # Create a quantum circuit with 2 qubits 15 | 16 | circuit = create_circuit(2) 17 | 18 | ## Step 2: Simulate the Circuit 19 | 20 | ```python 21 | 1 from src.quantum.quantum_circuits import simulate_circuit 22 | 2 23 | 3 # Simulate the circuit and get results 24 | 4 results = simulate_circuit(circuit) 25 | 5 print("Simulation Results:", results) 26 | ``` 27 | 28 | 29 | # Conclusion 30 | You have successfully created and simulated a quantum circuit! For more advanced topics, check out the next tutorials. 31 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/bug_report.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Bug report 3 | about: Create a report to help us improve 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | **Describe the bug** 11 | A clear and concise description of what the bug is. 12 | 13 | **To Reproduce** 14 | Steps to reproduce the behavior: 15 | 1. Go to '...' 16 | 2. Click on '....' 17 | 3. Scroll down to '....' 18 | 4. See error 19 | 20 | **Expected behavior** 21 | A clear and concise description of what you expected to happen. 22 | 23 | **Screenshots** 24 | If applicable, add screenshots to help explain your problem. 25 | 26 | **Desktop (please complete the following information):** 27 | - OS: [e.g. iOS] 28 | - Browser [e.g. chrome, safari] 29 | - Version [e.g. 22] 30 | 31 | **Smartphone (please complete the following information):** 32 | - Device: [e.g. iPhone6] 33 | - OS: [e.g. iOS8.1] 34 | - Browser [e.g. stock browser, safari] 35 | - Version [e.g. 22] 36 | 37 | **Additional context** 38 | Add any other context about the problem here. 39 | -------------------------------------------------------------------------------- /tests/test_data.py: -------------------------------------------------------------------------------- 1 | # tests/test_data.py 2 | 3 | import unittest 4 | from data_module import DataHandler # Hypothetical data handling module 5 | 6 | class TestDataHandler(unittest.TestCase): 7 | 8 | def setUp(self): 9 | self.data_handler = DataHandler() 10 | 11 | def test_load_data(self): 12 | data = self.data_handler.load_data('data/sample_data.csv') 13 | self.assertIsNotNone(data) 14 | self.assertGreater(len(data), 0) 15 | 16 | def test_process_data(self): 17 | raw_data = [1, 2, None, 4, 5] 18 | processed_data = self.data_handler.process_data(raw_data) 19 | self.assertEqual(len(processed_data), 4) # Assuming None values are removed 20 | 21 | def test_validate_data(self): 22 | valid_data = [1, 2, 3] 23 | invalid_data = [1, 'two', 3] 24 | self.assertTrue(self.data_handler.validate_data(valid_data)) 25 | self.assertFalse(self.data_handler.validate_data(invalid_data)) 26 | 27 | if __name__ == '__main__': 28 | unittest.main() 29 | -------------------------------------------------------------------------------- /docs/tutorials/tutorial_2.md: -------------------------------------------------------------------------------- 1 | # Tutorial 2: Building a Neural Network 2 | 3 | In this tutorial, you will learn how to build and train a neural network using the Quantum Celestia Nexus framework. 4 | 5 | ## Prerequisites 6 | - Ensure you have installed the required dependencies as outlined in the [installation instructions](../README.md). 7 | 8 | ## Step 1: Load Your Data 9 | 10 | ```python 11 | 1 from src.data.data_loader import load_data 12 | 2 13 | 3 # Load your dataset 14 | 4 features, labels = load_data('path/to/your/data.csv') 15 | ``` 16 | 17 | ## Step 2: Build the Neural Network 18 | 19 | ```python 20 | 1 from src.ai.neural_networks import build_model 21 | 2 22 | 3 # Build the model 23 | 4 model = build_model(input_shape=(features.shape[1],), num_classes=10) 24 | ``` 25 | 26 | ## Step 3: Train the Model 27 | 28 | ```python 29 | 1 model.fit(features, labels, epochs=10, batch_size=32) 30 | ``` 31 | 32 | # Conclusion 33 | 34 | You have successfully built and trained a neural network! Explore more advanced techniques in the following tutorials. 35 | -------------------------------------------------------------------------------- /scripts/setup_environment.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # setup_environment.sh 4 | 5 | # Exit immediately if a command exits with a non-zero status 6 | set -e 7 | 8 | # Define the Python version and virtual environment name 9 | PYTHON_VERSION="3.9" 10 | VENV_NAME="quantum_ai_env" 11 | 12 | # Update package list and install prerequisites 13 | echo "Updating package list..." 14 | sudo apt-get update 15 | 16 | echo "Installing Python and pip..." 17 | sudo apt-get install -y python${PYTHON_VERSION} python3-pip python3-venv 18 | 19 | # Create a virtual environment 20 | echo "Creating virtual environment: ${VENV_NAME}..." 21 | python${PYTHON_VERSION} -m venv ${VENV_NAME} 22 | 23 | # Activate the virtual environment 24 | echo "Activating virtual environment..." 25 | source ${VENV_NAME}/bin/activate 26 | 27 | # Install required Python packages 28 | echo "Installing required Python packages..." 29 | pip install -r requirements.txt 30 | 31 | echo "Development environment setup complete!" 32 | echo "To activate the virtual environment, run: source ${VENV_NAME}/bin/activate" 33 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 KOSASIH 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /examples/quantum_example.py: -------------------------------------------------------------------------------- 1 | # examples/quantum_example.py 2 | 3 | from communication.quantum_communication import QuantumKeyDistribution 4 | 5 | def main(): 6 | print("=== Quantum Key Distribution Example ===") 7 | 8 | # Initialize QKD with a specified number of bits 9 | num_bits = 10 10 | qkd = QuantumKeyDistribution(num_bits) 11 | 12 | # Step 1: Generate sender bits and bases 13 | qkd.generate_sender_bits() 14 | print("Sender Bits:", qkd.sender_bits) 15 | print("Sender Bases:", qkd.basis_sender) 16 | 17 | # Step 2: Simulate receiver's measurement 18 | qkd.simulate_receiver() 19 | print("Receiver Bits:", qkd.receiver_bits) 20 | print("Receiver Bases:", qkd.basis_receiver) 21 | 22 | # Step 3: Sift the key 23 | qkd.sift_key() 24 | print("Sifted Key:", qkd.get_key()) 25 | 26 | # Step 4: Perform error correction 27 | qkd.error_correction() 28 | print("Key after Error Correction:", qkd.get_key()) 29 | 30 | # Step 5: Perform privacy amplification 31 | qkd.privacy_amplification() 32 | print("Final Key after Privacy Amplification:", qkd.get_key()) 33 | 34 | if __name__ == "__main__": 35 | main() 36 | -------------------------------------------------------------------------------- /scripts/run_experiments.py: -------------------------------------------------------------------------------- 1 | # run_experiments.py 2 | 3 | import argparse 4 | from ai_module import AIModel # Hypothetical AI module 5 | from communication.quantum_communication import QuantumKeyDistribution 6 | 7 | def run_ai_experiment(): 8 | print("=== Running AI Experiment ===") 9 | model = AIModel() 10 | model.train() 11 | accuracy = model.evaluate() 12 | print(f"Model Accuracy: {accuracy}") 13 | 14 | def run_quantum_experiment(num_bits=10): 15 | print("=== Running Quantum Experiment ===") 16 | qkd = QuantumKeyDistribution(num_bits) 17 | qkd.generate_sender_bits() 18 | qkd.simulate_receiver() 19 | qkd.sift_key() 20 | qkd.error_correction() 21 | qkd.privacy_amplification() 22 | print("Final Key:", qkd.get_key()) 23 | 24 | def main(): 25 | parser = argparse.ArgumentParser(description="Run experiments and simulations.") 26 | parser.add_argument('--experiment', choices=['ai', 'quantum'], required=True, 27 | help="Specify the type of experiment to run.") 28 | parser.add_argument('--num_bits', type=int, default=10, 29 | help="Number of bits for quantum experiment (default: 10).") 30 | 31 | args = parser.parse_args() 32 | 33 | if args.experiment == 'ai': 34 | run_ai_experiment() 35 | elif args.experiment == 'quantum': 36 | run_quantum_experiment(args.num_bits) 37 | 38 | if __name__ == "__main__": 39 | main() 40 | -------------------------------------------------------------------------------- /tests/test_communication.py: -------------------------------------------------------------------------------- 1 | # tests/test_communication.py 2 | 3 | import unittest 4 | from communication.secure_transmission import SecureTransmission 5 | 6 | class TestSecureTransmission(unittest.TestCase): 7 | 8 | def setUp(self): 9 | self.secure_transmission = SecureTransmission() 10 | 11 | def test_aes_encryption_decryption(self): 12 | message = "This is a secret message." 13 | encrypted_message = self.secure_transmission.encrypt_aes(message) 14 | decrypted_message = self.secure_transmission.decrypt_aes(encrypted_message) 15 | self.assertEqual(message, decrypted_message) 16 | 17 | def test_rsa_encryption_decryption(self): 18 | message = "This is a secret message." 19 | public_key, private_key = self.secure_transmission.generate_rsa_keys() 20 | encrypted_message = self.secure_transmission.encrypt_rsa(message, public_key) 21 | decrypted_message = self.secure_transmission.decrypt_rsa(encrypted_message, private_key) 22 | self.assertEqual(message, decrypted_message) 23 | 24 | def test_digital_signature(self): 25 | message = "This is a message to sign." 26 | public_key, private_key = self.secure_transmission.generate_rsa_keys() 27 | signature = self.secure_transmission.sign_message(message, private_key) 28 | is_valid = self.secure_transmission.verify_signature(message, signature, public_key) 29 | self.assertTrue(is_valid) 30 | 31 | if __name__ == '__main__': 32 | unittest.main() 33 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | # Core Libraries 2 | numpy==1.23.5 # For numerical operations 3 | pandas==1.5.3 # For data manipulation and analysis 4 | scikit-learn==1.2.0 # For machine learning algorithms 5 | scipy==1.9.3 # For scientific computing 6 | 7 | # AI and Machine Learning 8 | tensorflow==2.11.0 # For deep learning (if using TensorFlow) 9 | torch==1.13.0 # For deep learning (if using PyTorch) 10 | joblib==1.2.0 # For saving and loading models 11 | 12 | # Quantum Computing 13 | qiskit==0.39.1 # For quantum computing simulations and algorithms 14 | pennylane==0.24.0 # For quantum machine learning 15 | 16 | # Secure Communication 17 | cryptography==38.0.1 # For encryption and secure communication 18 | pycryptodome==3.15.0 # For cryptographic functions 19 | requests==2.28.1 # For making HTTP requests (if needed for communication) 20 | 21 | # Testing 22 | pytest==7.2.0 # For running tests 23 | pytest-cov==3.0.0 # For test coverage reporting 24 | 25 | # Development Tools 26 | black==22.3.0 # For code formatting 27 | flake8==4.0.1 # For linting 28 | mypy==0.991 # For type checking 29 | 30 | # Optional: Jupyter Notebook (if needed for experimentation) 31 | jupyter==1.0.0 # For interactive computing 32 | 33 | # Optional: Visualization 34 | matplotlib==3.6.0 # For plotting and visualization 35 | seaborn==0.11.2 # For statistical data visualization 36 | -------------------------------------------------------------------------------- /docs/architecture.md: -------------------------------------------------------------------------------- 1 | # System Architecture 2 | 3 | The Quantum Celestia Nexus project is designed to integrate quantum computing with advanced artificial intelligence. The system architecture is divided into four main modules: 4 | 5 | ## Quantum Module 6 | - Responsible for creating and simulating quantum circuits. 7 | - Utilizes quantum computing principles to solve complex problems. 8 | 9 | ## AI Module 10 | - Handles the construction and training of neural networks. 11 | - Employs machine learning algorithms to analyze data. 12 | 13 | ## Data Module 14 | - Manages data loading, processing, and storage. 15 | - Ensures data integrity and security. 16 | 17 | ## Communication Module 18 | - Facilitates secure quantum communication between parties. 19 | - Utilizes quantum key distribution protocols for encryption. 20 | 21 | ## System Flow 22 | 1. **Data Ingestion**: The Data Module loads and processes data from various sources. 23 | 2. **Quantum Circuit Creation**: The Quantum Module creates a quantum circuit based on the problem requirements. 24 | 3. **Neural Network Construction**: The AI Module builds a neural network to analyze the data. 25 | 4. **Quantum Simulation**: The Quantum Module simulates the quantum circuit to obtain results. 26 | 5. **Data Analysis**: The AI Module trains the neural network using the simulation results. 27 | 6. **Secure Communication**: The Communication Module sends the results securely using quantum key distribution. 28 | 29 | ## Conclusion 30 | The Quantum Celestia Nexus project's modular architecture enables seamless integration of quantum computing and artificial intelligence, providing a robust framework for solving complex scientific problems. 31 | -------------------------------------------------------------------------------- /tests/test_quantum.py: -------------------------------------------------------------------------------- 1 | # tests/test_quantum.py 2 | 3 | import unittest 4 | from communication.quantum_communication import QuantumKeyDistribution 5 | 6 | class TestQuantumKeyDistribution(unittest.TestCase): 7 | 8 | def setUp(self): 9 | self.qkd = QuantumKeyDistribution(num_bits=10) 10 | 11 | def test_generate_sender_bits(self): 12 | self.qkd.generate_sender_bits() 13 | self.assertEqual(len(self.qkd.sender_bits), 10) 14 | self.assertTrue(all(bit in [0, 1] for bit in self.qkd.sender_bits)) 15 | 16 | def test_simulate_receiver(self): 17 | self.qkd.generate_sender_bits() 18 | self.qkd.simulate_receiver() 19 | self.assertEqual(len(self.qkd.receiver_bits), 10) 20 | self.assertTrue(all(bit in [0, 1] for bit in self.qkd.receiver_bits)) 21 | 22 | def test_sift_key(self): 23 | self.qkd.generate_sender_bits() 24 | self.qkd.simulate_receiver() 25 | self.qkd.sift_key() 26 | self.assertIsNotNone(self.qkd.get_key()) 27 | 28 | def test_error_correction(self): 29 | self.qkd.generate_sender_bits() 30 | self.qkd.simulate_receiver() 31 | self.qkd.sift_key() 32 | original_key = self.qkd.get_key() 33 | self.qkd.error_correction() 34 | self.assertNotEqual(original_key, self.qkd.get_key()) 35 | 36 | def test_privacy_amplification(self): 37 | self.qkd.generate_sender_bits() 38 | self.qkd.simulate_receiver() 39 | self.qkd.sift_key() 40 | original_key = self.qkd.get_key() 41 | self.qkd.privacy_amplification() 42 | self.assertNotEqual(original_key, self.qkd.get_key()) 43 | 44 | if __name__ == '__main__': 45 | unittest.main() 46 | -------------------------------------------------------------------------------- /examples/communication_example.py: -------------------------------------------------------------------------------- 1 | # examples/communication_example.py 2 | 3 | from communication.secure_transmission import SecureTransmission 4 | 5 | def main(): 6 | print("=== Communication Protocols Example ===") 7 | 8 | # Initialize the secure transmission module 9 | secure_transmission = SecureTransmission() 10 | 11 | # Example message for AES encryption 12 | message_aes = "This is a secret message for AES." 13 | encrypted_message_aes = secure_transmission.encrypt_aes(message_aes) 14 | decrypted_message_aes = secure_transmission.decrypt_aes(encrypted_message_aes) 15 | print("Original AES Message:", message_aes) 16 | print("Encrypted AES Message:", encrypted_message_aes) 17 | print("Decrypted AES Message:", decrypted_message_aes) 18 | 19 | # Example message for RSA encryption 20 | message_rsa = "This is a secret message for RSA." 21 | public_key, private_key = secure_transmission.generate_rsa_keys() 22 | encrypted_message_rsa = secure_transmission.encrypt_rsa(message_rsa, public_key) 23 | decrypted_message_rsa = secure_transmission.decrypt_rsa(encrypted_message_rsa, private_key) 24 | print("Original RSA Message:", message_rsa) 25 | print("Encrypted RSA Message:", encrypted_message_rsa) 26 | print("Decrypted RSA Message:", decrypted_message_rsa) 27 | 28 | # Digital signature example 29 | message_signature = "This is a message to sign." 30 | signature = secure_transmission.sign_message(message_signature, private_key) 31 | is_valid = secure_transmission.verify_signature(message_signature, signature, public_key) 32 | print("Signature:", signature) 33 | print("Is the signature valid?", is_valid) 34 | 35 | if __name__ == "__main__": 36 | main() 37 | -------------------------------------------------------------------------------- /docs/API_reference.md: -------------------------------------------------------------------------------- 1 | # API Reference 2 | 3 | This document provides a detailed reference for the APIs available in the Quantum Celestia Nexus project. 4 | 5 | ## Quantum Module 6 | 7 | ### `quantum_circuits.py` 8 | 9 | #### `create_circuit(qubits: int) -> QuantumCircuit` 10 | Creates a quantum circuit with the specified number of qubits. 11 | 12 | **Parameters:** 13 | - `qubits`: The number of qubits in the circuit. 14 | 15 | **Returns:** 16 | - A QuantumCircuit object. 17 | 18 | #### `simulate_circuit(circuit: QuantumCircuit) -> List[float]` 19 | Simulates the given quantum circuit and returns the measurement results. 20 | 21 | **Parameters:** 22 | - `circuit`: The QuantumCircuit object to simulate. 23 | 24 | **Returns:** 25 | - A list of measurement results. 26 | 27 | ## AI Module 28 | 29 | ### `neural_networks.py` 30 | 31 | #### `build_model(input_shape: Tuple[int], num_classes: int) -> Model` 32 | Builds a neural network model with the specified input shape and number of output classes. 33 | 34 | **Parameters:** 35 | - `input_shape`: The shape of the input data. 36 | - `num_classes`: The number of output classes. 37 | 38 | **Returns:** 39 | - A compiled Keras Model object. 40 | 41 | ## Data Module 42 | 43 | ### `data_loader.py` 44 | 45 | #### `load_data(file_path: str) -> Tuple[np.ndarray, np.ndarray]` 46 | Loads data from the specified file path. 47 | 48 | **Parameters:** 49 | - `file_path`: The path to the data file. 50 | 51 | **Returns:** 52 | - A tuple containing the features and labels as NumPy arrays. 53 | 54 | ## Communication Module 55 | 56 | ### `quantum_communication.py` 57 | 58 | #### `send_message(message: str, recipient: str) -> bool` 59 | Sends a quantum message to the specified recipient. 60 | 61 | **Parameters:** 62 | - `message`: The message to send. 63 | - `recipient`: The recipient's address. 64 | 65 | **Returns:** 66 | - A boolean indicating success or failure. 67 | -------------------------------------------------------------------------------- /src/quantum/quantum_circuits.py: -------------------------------------------------------------------------------- 1 | from typing import List, Optional, Tuple 2 | import numpy as np 3 | from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister, execute, Aer 4 | from qiskit.circuit import Parameter 5 | from qiskit.quantum_info import Statevector 6 | from .quantum_utils import validate_circuit, optimize_circuit 7 | 8 | class QuantumCircuitDesigner: 9 | """Advanced Quantum Circuit Designer with optimization capabilities.""" 10 | 11 | def __init__(self, num_qubits: int, num_classical_bits: Optional[int] = None): 12 | self.num_qubits = num_qubits 13 | self.num_classical_bits = num_classical_bits or num_qubits 14 | self.quantum_register = QuantumRegister(num_qubits, 'q') 15 | self.classical_register = ClassicalRegister(self.num_classical_bits, 'c') 16 | self.circuit = QuantumCircuit(self.quantum_register, self.classical_register) 17 | self.parameters = [] 18 | 19 | def add_parametric_gates(self, gate_type: str, qubit: int, params: List[float]) -> None: 20 | """Add parametric quantum gates with optimization.""" 21 | if gate_type == "rx": 22 | theta = Parameter(f'θ_{len(self.parameters)}') 23 | self.circuit.rx(theta, qubit) 24 | self.parameters.append((theta, params[0])) 25 | elif gate_type == "ry": 26 | phi = Parameter(f'φ_{len(self.parameters)}') 27 | self.circuit.ry(phi, qubit) 28 | self.parameters.append((phi, params[0])) 29 | elif gate_type == "rz": 30 | lambda_param = Parameter(f'λ_{len(self.parameters)}') 31 | self.circuit.rz(lambda_param, qubit) 32 | self.parameters.append((lambda_param, params[0])) 33 | 34 | def add_entanglement_layer(self, connectivity: str = 'full') -> None: 35 | """Add an entanglement layer with specified connectivity.""" 36 | if connectivity == 'full': 37 | for i in range(self.num_qubits): 38 | for j in range(i + 1, self.num_qubits): 39 | self.circuit.cx(i, j) 40 | elif connectivity == 'linear': 41 | for i in range(self.num_qubits - 1): 42 | self.circuit.cx(i, i + 1) 43 | 44 | def apply_quantum_error_correction(self) -> None: 45 | """Apply quantum error correction codes.""" 46 | # Implementation of Surface code or Shor's code 47 | pass 48 | 49 | def simulate(self, shots: int = 1000, backend_name: str = 'qasm_simulator') -> dict: 50 | """Simulate the quantum circuit with specified parameters.""" 51 | try: 52 | backend = Aer.get_backend(backend_name) 53 | optimized_circuit = optimize_circuit(self.circuit) 54 | bound_circuit = optimized_circuit.bind_parameters( 55 | {param: value for param, value in self.parameters} 56 | ) 57 | validate_circuit(bound_circuit) 58 | result = execute(bound_circuit, backend, shots=shots).result() 59 | return result.get_counts() 60 | except Exception as e: 61 | raise QuantumCircuitError(f"Simulation failed: {str(e)}") 62 | 63 | class QuantumCircuitError(Exception): 64 | """Custom exception for quantum circuit operations.""" 65 | pass 66 | -------------------------------------------------------------------------------- /src/communication/quantum_communication.py: -------------------------------------------------------------------------------- 1 | # communication/quantum_communication.py 2 | 3 | import numpy as np 4 | import random 5 | from typing import List, Tuple 6 | 7 | class QuantumKeyDistribution: 8 | def __init__(self, num_bits: int): 9 | """ 10 | Initialize the Quantum Key Distribution (QKD) protocol. 11 | :param num_bits: Number of bits to be transmitted. 12 | """ 13 | self.num_bits = num_bits 14 | self.sender_bits = None 15 | self.basis_sender = None 16 | self.basis_receiver = None 17 | self.receiver_bits = None 18 | self.key = None 19 | 20 | def generate_sender_bits(self): 21 | """Generate random bits and random bases for the sender.""" 22 | self.sender_bits = np.random.randint(0, 2, self.num_bits) 23 | self.basis_sender = np.random.randint(0, 2, self.num_bits) # 0 for Z-basis, 1 for X-basis 24 | 25 | def simulate_receiver(self): 26 | """Simulate the receiver's measurement.""" 27 | self.basis_receiver = np.random.randint(0, 2, self.num_bits) # Random basis for the receiver 28 | self.receiver_bits = np.zeros(self.num_bits) 29 | 30 | for i in range(self.num_bits): 31 | if self.basis_receiver[i] == self.basis_sender[i]: 32 | self.receiver_bits[i] = self.sender_bits[i] # Correct measurement 33 | else: 34 | self.receiver_bits[i] = random.randint(0, 1) # Random measurement 35 | 36 | def sift_key(self): 37 | """Sift the key based on matching bases.""" 38 | key_bits = [] 39 | for i in range(self.num_bits): 40 | if self.basis_sender[i] == self.basis_receiver[i]: 41 | key_bits.append(int(self.sender_bits[i])) 42 | self.key = np.array(key_bits) 43 | 44 | def error_correction(self) -> None: 45 | """ 46 | Perform error correction on the key. 47 | This is a placeholder for a more complex error correction algorithm. 48 | """ 49 | # In a real implementation, you would use a method like Cascade or LDPC codes. 50 | print("Performing error correction (placeholder).") 51 | 52 | def privacy_amplification(self) -> None: 53 | """ 54 | Perform privacy amplification on the key. 55 | This is a placeholder for a more complex privacy amplification algorithm. 56 | """ 57 | # In a real implementation, you would use a method like universal hashing. 58 | print("Performing privacy amplification (placeholder).") 59 | 60 | def get_key(self) -> List[int]: 61 | """Return the generated key.""" 62 | return self.key.tolist() if self.key is not None else [] 63 | 64 | # Example usage 65 | if __name__ == "__main__": 66 | num_bits = 10 # Number of bits to transmit 67 | qkd = QuantumKeyDistribution(num_bits) 68 | 69 | # Step 1: Generate sender bits and bases 70 | qkd.generate_sender_bits() 71 | print("Sender Bits:", qkd.sender_bits) 72 | print("Sender Bases:", qkd.basis_sender) 73 | 74 | # Step 2: Simulate receiver's measurement 75 | qkd.simulate_receiver() 76 | print("Receiver Bits:", qkd.receiver_bits) 77 | print("Receiver Bases:", qkd.basis_receiver) 78 | 79 | # Step 3: Sift the key 80 | qkd.sift_key() 81 | print("Sifted Key:", qkd.get_key()) 82 | 83 | # Step 4: Perform error correction 84 | qkd.error_correction() 85 | 86 | # Step 5: Perform privacy amplification 87 | qkd.privacy_amplification() 88 | -------------------------------------------------------------------------------- /src/main.py: -------------------------------------------------------------------------------- 1 | # main.py 2 | 3 | import argparse 4 | import logging 5 | from src.communication.quantum_communication import QuantumKeyDistribution 6 | from src.blockchain.blockchain import Blockchain 7 | from src.ai_model import AIModel 8 | from src.utils import generate_nonce, validate_transaction # Hypothetical utility functions 9 | 10 | # Configure logging 11 | logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') 12 | 13 | def initialize_blockchain(): 14 | """Initialize the blockchain.""" 15 | logging.info("Initializing the blockchain...") 16 | blockchain = Blockchain() 17 | return blockchain 18 | 19 | def run_quantum_key_distribution(num_bits): 20 | """Run Quantum Key Distribution.""" 21 | logging.info("=== Running Quantum Key Distribution ===") 22 | qkd = QuantumKeyDistribution(num_bits) 23 | qkd.generate_sender_bits() 24 | qkd.simulate_receiver() 25 | qkd.sift_key() 26 | qkd.error_correction() 27 | qkd.privacy_amplification() 28 | final_key = qkd.get_key() 29 | logging.info("Final Key from QKD: %s", final_key) 30 | return final_key 31 | 32 | def deploy_ai_model(): 33 | """Deploy the AI model.""" 34 | logging.info("=== Deploying AI Model ===") 35 | model = AIModel() 36 | model.train() 37 | model.save("quantum_ai_model") 38 | logging.info("AI Model deployed and saved.") 39 | 40 | def create_transaction(blockchain, sender, receiver, amount, key): 41 | """Create a transaction on the blockchain.""" 42 | logging.info(f"Creating transaction from {sender} to {receiver} for amount {amount}...") 43 | nonce = generate_nonce() # Generate a unique nonce for the transaction 44 | transaction = blockchain.create_transaction(sender, receiver, amount, key, nonce) 45 | 46 | if validate_transaction(transaction): 47 | blockchain.add_transaction(transaction) 48 | logging.info("Transaction created and added to the blockchain: %s", transaction) 49 | else: 50 | logging.error("Transaction validation failed.") 51 | 52 | def main(): 53 | parser = argparse.ArgumentParser(description="Quantum Celestia Nexus Main Application") 54 | parser.add_argument('--num_bits', type=int, default=10, help="Number of bits for Quantum Key Distribution.") 55 | parser.add_argument('--deploy_ai', action='store_true', help="Deploy the AI model.") 56 | parser.add_argument('--sender', type=str, required=True, help="Sender's address.") 57 | parser.add_argument('--receiver', type=str, required=True, help="Receiver's address.") 58 | parser.add_argument('--amount', type=float, required=True, help="Amount to transfer.") 59 | parser.add_argument('--simulate', action='store_true', help="Simulate a transaction without adding to the blockchain.") 60 | 61 | args = parser.parse_args() 62 | 63 | # Initialize the blockchain 64 | blockchain = initialize_blockchain() 65 | 66 | # Run Quantum Key Distribution 67 | final_key = run_quantum_key_distribution(args.num_bits) 68 | 69 | # Optionally deploy the AI model 70 | if args.deploy_ai: 71 | deploy_ai_model() 72 | 73 | # Create a transaction on the blockchain 74 | if args.simulate: 75 | logging.info("Simulating transaction...") 76 | logging.info("Transaction details: Sender: %s, Receiver: %s, Amount: %s", args.sender, args.receiver, args.amount) 77 | else: 78 | create_transaction(blockchain, args.sender, args.receiver, args.amount, final_key) 79 | 80 | logging.info("=== Application Finished ===") 81 | 82 | if __name__ == "__main__": 83 | main() 84 | -------------------------------------------------------------------------------- /src/ai/inference.py: -------------------------------------------------------------------------------- 1 | # src/ai/inference.py 2 | 3 | import numpy as np 4 | import tensorflow as tf 5 | from tensorflow.keras.models import load_model 6 | import matplotlib.pyplot as plt 7 | import os 8 | 9 | class ModelInference: 10 | def __init__(self, model_path): 11 | self.model = load_model(model_path) 12 | 13 | def predict(self, input_data): 14 | """ 15 | Make predictions on the input data. 16 | :param input_data: Numpy array of shape (num_samples, height, width, channels) 17 | :return: Predicted class indices 18 | """ 19 | predictions = self.model.predict(input_data) 20 | return np.argmax(predictions, axis=1) 21 | 22 | def evaluate(self, test_data, test_labels): 23 | """ 24 | Evaluate the model on test data. 25 | :param test_data: Numpy array of test images 26 | :param test_labels: Numpy array of true labels 27 | :return: Dictionary containing loss and accuracy 28 | """ 29 | loss, accuracy = self.model.evaluate(test_data, test_labels) 30 | return {'loss': loss, 'accuracy': accuracy} 31 | 32 | def visualize_predictions(self, input_data, true_labels, num_images=5): 33 | """ 34 | Visualize predictions made by the model. 35 | :param input_data: Numpy array of input images 36 | :param true_labels: Numpy array of true labels 37 | :param num_images: Number of images to visualize 38 | """ 39 | predictions = self.predict(input_data) 40 | 41 | plt.figure(figsize=(15, 5)) 42 | for i in range(num_images): 43 | plt.subplot(1, num_images, i + 1) 44 | plt.imshow(input_data[i]) 45 | plt.title(f'True: {true_labels[i]}, Pred: {predictions[i]}') 46 | plt.axis('off') 47 | plt.show() 48 | 49 | def batch_predict(self, image_dir, img_size=(224, 224)): 50 | """ 51 | Predict classes for a batch of images in a directory. 52 | :param image_dir: Directory containing images 53 | :param img_size: Size to which images will be resized 54 | :return: Dictionary of image filenames and their predicted classes 55 | """ 56 | from tensorflow.keras.preprocessing.image import load_img, img_to_array 57 | 58 | predictions = {} 59 | for filename in os.listdir(image_dir): 60 | if filename.endswith(('.png', '.jpg', '.jpeg')): 61 | img_path = os.path.join(image_dir, filename) 62 | img = load_img(img_path, target_size=img_size) 63 | img_array = img_to_array(img) / 255.0 # Normalize 64 | img_array = np.expand_dims(img_array, axis=0) # Add batch dimension 65 | 66 | pred = self.predict(img_array) 67 | predictions[filename] = pred[0] 68 | 69 | return predictions 70 | 71 | # Example usage 72 | if __name__ == "__main__": 73 | model_path = 'best_model.h5' # Path to your trained model 74 | inference = ModelInference(model_path) 75 | 76 | # Example for evaluating on test data 77 | test_data = np.random.rand(10, 224, 224, 3) # Replace with actual test data 78 | test_labels = np.random.randint(0, 10, size=(10,)) # Replace with actual labels 79 | evaluation_results = inference.evaluate(test_data, test_labels) 80 | print(f"Test Loss: {evaluation_results['loss']}, Test Accuracy: {evaluation_results['accuracy']}") 81 | 82 | # Example for visualizing predictions 83 | inference.visualize_predictions(test_data, test_labels, num_images=5) 84 | 85 | # Example for batch prediction 86 | image_dir = 'path/to/image_directory' # Replace with your image directory 87 | predictions = inference.batch_predict(image_dir) 88 | for filename, pred in predictions.items(): 89 | print(f"{filename}: Predicted Class: {pred}") 90 | -------------------------------------------------------------------------------- /src/quantum/quantum_algorithms.py: -------------------------------------------------------------------------------- 1 | from typing import List, Dict, Optional, Union 2 | import numpy as np 3 | from qiskit import QuantumCircuit 4 | from .quantum_circuits import QuantumCircuitDesigner 5 | from .quantum_utils import measure_state_fidelity 6 | 7 | class QuantumAlgorithmFactory: 8 | """Factory class for implementing various quantum algorithms.""" 9 | 10 | @staticmethod 11 | def create_algorithm(algorithm_type: str, **kwargs) -> 'QuantumAlgorithm': 12 | """Factory method to create quantum algorithm instances.""" 13 | algorithms = { 14 | 'grover': GroverSearch, 15 | 'shor': ShorFactorization, 16 | 'vqe': VariationalQuantumEigensolver, 17 | 'qft': QuantumFourierTransform 18 | } 19 | return algorithms[algorithm_type](**kwargs) 20 | 21 | class QuantumAlgorithm: 22 | """Base class for quantum algorithms.""" 23 | def __init__(self, num_qubits: int): 24 | self.num_qubits = num_qubits 25 | self.circuit_designer = QuantumCircuitDesigner(num_qubits) 26 | 27 | def run(self) -> Dict: 28 | """Execute the quantum algorithm.""" 29 | raise NotImplementedError 30 | 31 | class GroverSearch(QuantumAlgorithm): 32 | """Implementation of Grover's Search Algorithm.""" 33 | 34 | def __init__(self, num_qubits: int, target_state: str): 35 | super().__init__(num_qubits) 36 | self.target_state = target_state 37 | self.oracle = self._construct_oracle() 38 | 39 | def _construct_oracle(self) -> QuantumCircuit: 40 | """Construct the oracle for the target state.""" 41 | oracle_circuit = QuantumCircuit(self.num_qubits) 42 | # Implementation of oracle construction 43 | return oracle_circuit 44 | 45 | def run(self, iterations: Optional[int] = None) -> Dict: 46 | """Execute Grover's search algorithm.""" 47 | if iterations is None: 48 | iterations = int(np.pi/4 * np.sqrt(2**self.num_qubits)) 49 | 50 | # Initialize superposition 51 | for qubit in range(self.num_qubits): 52 | self.circuit_designer.circuit.h(qubit) 53 | 54 | # Apply Grover iteration 55 | for _ in range(iterations): 56 | # Apply oracle 57 | self.circuit_designer.circuit.compose(self.oracle, inplace=True) 58 | # Apply diffusion operator 59 | self._apply_diffusion() 60 | 61 | return self.circuit_designer.simulate(shots=1000) 62 | 63 | def _apply_diffusion(self) -> None: 64 | """Apply the diffusion operator.""" 65 | # Implementation of diffusion operator 66 | pass 67 | 68 | class VariationalQuantumEigensolver(QuantumAlgorithm): 69 | """Implementation of Variational Quantum Eigensolver.""" 70 | 71 | def __init__(self, num_qubits: int, hamiltonian: np.ndarray, max_iterations: int = 100): 72 | super().__init__(num_qubits) 73 | self.hamiltonian = hamiltonian 74 | self.max_iterations = max_iterations 75 | self.optimizer = self._initialize_optimizer() 76 | 77 | def _initialize_optimizer(self): 78 | """Initialize classical optimizer.""" 79 | # Implementation of classical optimizer 80 | pass 81 | 82 | def compute_expectation_value(self, parameters: List[float]) -> float: 83 | """Compute expectation value of the Hamiltonian.""" 84 | # Implementation of expectation value computation 85 | pass 86 | 87 | def run(self) -> Dict: 88 | """Execute VQE algorithm.""" 89 | current_params = np.random.random(self.num_qubits * 3) # Initial parameters 90 | 91 | for iteration in range(self.max_iterations): 92 | expectation = self.compute_expectation_value(current_params) 93 | new_params = self.optimizer.step(current_params, expectation) 94 | 95 | if self._convergence_reached(current_params, new_params): 96 | break 97 | 98 | current_params = new_params 99 | 100 | return { 101 | 'optimal_parameters': current_params, 102 | 'ground_state_energy': self.compute_expectation_value(current_params) 103 | } 104 | 105 | def _convergence_reached(self, old_params: np.ndarray, new_params: np.ndarray) -> bool: 106 | """Check if convergence is reached.""" 107 | return np.allclose(old_params, new_params, rtol=1e-5) 108 | -------------------------------------------------------------------------------- /src/data/data_visualization.py: -------------------------------------------------------------------------------- 1 | # data/data_visualization.py 2 | 3 | import matplotlib.pyplot as plt 4 | import seaborn as sns 5 | import pandas as pd 6 | from typing import List, Dict, Any 7 | 8 | class DataVisualizer: 9 | def __init__(self, df: pd.DataFrame): 10 | """ 11 | Initialize the DataVisualizer with a DataFrame. 12 | :param df: DataFrame to visualize. 13 | """ 14 | self.df = df 15 | 16 | def plot_distribution(self, column: str, bins: int = 30, kde: bool = True) -> None: 17 | """ 18 | Plot the distribution of a specified column. 19 | :param column: Column name to visualize. 20 | :param bins: Number of bins for the histogram. 21 | :param kde: Whether to include a Kernel Density Estimate (KDE) plot. 22 | """ 23 | plt.figure(figsize=(10, 6)) 24 | sns.histplot(self.df[column], bins=bins, kde=kde) 25 | plt.title(f'Distribution of {column}') 26 | plt.xlabel(column) 27 | plt.ylabel('Frequency') 28 | plt.grid() 29 | plt.show() 30 | 31 | def plot_boxplot(self, column: str) -> None: 32 | """ 33 | Plot a boxplot for a specified column. 34 | :param column: Column name to visualize. 35 | """ 36 | plt.figure(figsize=(10, 6)) 37 | sns.boxplot(x=self.df[column]) 38 | plt.title(f'Boxplot of {column}') 39 | plt.xlabel(column) 40 | plt.grid() 41 | plt.show() 42 | 43 | def plot_correlation_matrix(self) -> None: 44 | """ 45 | Plot the correlation matrix of the DataFrame. 46 | """ 47 | plt.figure(figsize=(12, 8)) 48 | correlation = self.df.corr() 49 | sns.heatmap(correlation, annot=True, fmt=".2f", cmap='coolwarm', square=True) 50 | plt.title('Correlation Matrix') 51 | plt.show() 52 | 53 | def plot_pairplot(self, hue: str = None) -> None: 54 | """ 55 | Plot pairwise relationships in the dataset. 56 | :param hue: Column name for color encoding. 57 | """ 58 | sns.pairplot(self.df, hue=hue) 59 | plt.title('Pairplot of the DataFrame') 60 | plt.show() 61 | 62 | def plot_time_series(self, time_column: str, value_column: str) -> None: 63 | """ 64 | Plot a time series for a specified time and value column. 65 | :param time_column: Column name for time. 66 | :param value_column: Column name for values. 67 | """ 68 | plt.figure(figsize=(14, 7)) 69 | plt.plot(self.df[time_column], self.df[value_column], marker='o') 70 | plt.title(f'Time Series of {value_column} over {time_column}') 71 | plt.xlabel(time_column) 72 | plt.ylabel(value_column) 73 | plt.grid() 74 | plt.show() 75 | 76 | def plot_category_counts(self, column: str) -> None: 77 | """ 78 | Plot counts of categories in a specified column. 79 | :param column: Column name to visualize. 80 | """ 81 | plt.figure(figsize=(12, 6)) 82 | sns.countplot(data=self.df, x=column, order=self.df[column].value_counts().index) 83 | plt.title(f'Count of Categories in {column}') 84 | plt.xticks(rotation=45) 85 | plt.grid() 86 | plt.show() 87 | 88 | def save_plot(self, filename: str) -> None: 89 | """ 90 | Save the current plot to a file. 91 | :param filename: Name of the file to save the plot. 92 | """ 93 | plt.savefig(filename) 94 | plt.close() 95 | 96 | def visualize_all(self, columns: List[str]) -> None: 97 | """ 98 | Visualize distributions and boxplots for a list of columns. 99 | :param columns: List of column names to visualize. 100 | """ 101 | for column in columns: 102 | self.plot_distribution(column) 103 | self.plot_boxplot(column) 104 | 105 | # Example usage 106 | if __name__ == "__main__": 107 | # Load data 108 | data_loader = DataLoader(data_dir='path/to/data') 109 | df = data_loader.load_data('data.csv', file_type='csv') 110 | 111 | # Visualize data 112 | visualizer = DataVisualizer(df) 113 | visualizer.plot_distribution('numerical_column') 114 | visualizer.plot_boxplot('numerical_column') 115 | visualizer.plot_correlation_matrix() 116 | visualizer.plot_pairplot(hue='target_column') 117 | visualizer.plot_time_series(time_column='date_column', value_column='value_column') 118 | visualizer.plot_category_counts(column='category_column') 119 | -------------------------------------------------------------------------------- /src/ai/training.py: -------------------------------------------------------------------------------- 1 | # src/ai/training.py 2 | 3 | import tensorflow as tf 4 | from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau 5 | from tensorflow.keras.preprocessing.image import ImageDataGenerator 6 | from .neural_networks import FeedforwardNN, ConvolutionalNN, TransferLearningModel 7 | 8 | class DataLoader: 9 | def __init__(self, train_dir, val_dir, img_size=(224, 224), batch_size=32): 10 | self.train_dir = train_dir 11 | self.val_dir = val_dir 12 | self.img_size = img_size 13 | self.batch_size = batch_size 14 | self.train_datagen = ImageDataGenerator( 15 | rescale=1.0/255, 16 | rotation_range=20, 17 | width_shift_range=0.2, 18 | height_shift_range=0.2, 19 | shear_range=0.2, 20 | zoom_range=0.2, 21 | horizontal_flip=True, 22 | fill_mode='nearest' 23 | ) 24 | self.val_datagen = ImageDataGenerator(rescale=1.0/255) 25 | 26 | def load_data(self): 27 | train_generator = self.train_datagen.flow_from_directory( 28 | self.train_dir, 29 | target_size=self.img_size, 30 | batch_size=self.batch_size, 31 | class_mode='sparse', 32 | shuffle=True 33 | ) 34 | val_generator = self.val_datagen.flow_from_directory( 35 | self.val_dir, 36 | target_size=self.img_size, 37 | batch_size=self.batch_size, 38 | class_mode='sparse', 39 | shuffle=False 40 | ) 41 | return train_generator, val_generator 42 | 43 | class ModelTrainer: 44 | def __init__(self, model, train_data, val_data): 45 | self.model = model 46 | self.train_data = train_data 47 | self.val_data = val_data 48 | 49 | def train(self, epochs=50, batch_size=32): 50 | checkpoint = ModelCheckpoint('best_model.h5', save_best_only=True, monitor='val_loss', mode='min') 51 | early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True) 52 | reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=3, min_lr=1e-6) 53 | 54 | history = self.model.fit(self.train_data, 55 | validation_data=self.val_data, 56 | epochs=epochs, 57 | batch_size=batch_size, 58 | callbacks=[checkpoint, early_stopping, reduce_lr]) 59 | return history 60 | 61 | def load_model(self, model_path='best_model.h5'): 62 | self.model.load_weights(model_path) 63 | 64 | def evaluate(self, test_data): 65 | loss, accuracy = self.model.evaluate(test_data) 66 | return {'loss': loss, 'accuracy': accuracy} 67 | 68 | def plot_training_history(self, history): 69 | import matplotlib.pyplot as plt 70 | 71 | plt.figure(figsize=(12, 4)) 72 | plt.subplot(1, 2, 1) 73 | plt.plot(history.history['accuracy'], label='Train Accuracy') 74 | plt.plot(history.history['val_accuracy'], label='Validation Accuracy') 75 | plt.title('Model Accuracy') 76 | plt.xlabel('Epoch') 77 | plt.ylabel('Accuracy') 78 | plt.legend() 79 | 80 | plt.subplot(1, 2, 2) 81 | plt.plot(history.history['loss'], label='Train Loss') 82 | plt.plot(history.history['val_loss'], label='Validation Loss') 83 | plt.title('Model Loss') 84 | plt.xlabel('Epoch') 85 | plt.ylabel('Loss') 86 | plt.legend() 87 | 88 | plt.tight_layout() 89 | plt.show() 90 | 91 | # Example usage 92 | if __name__ == "__main__": 93 | # Define paths to your training and validation data directories 94 | train_dir = 'path/to/train_data' 95 | val_dir = 'path/to/val_data' 96 | 97 | # Load data 98 | data_loader = DataLoader(train_dir, val_dir) 99 | train_data, val_data = data_loader.load_data() 100 | 101 | # Initialize and compile model 102 | model = ConvolutionalNN(input_shape=(224, 224, 3), num_classes=10) # Example for a CNN 103 | model.compile_model(learning_rate=0.001) 104 | 105 | # Train the model 106 | trainer = ModelTrainer(model.model, train_data, val_data) 107 | history = trainer.train(epochs=50) 108 | 109 | # Plot training history 110 | trainer.plot_training_history(history) 111 | 112 | # Evaluate the model on validation data 113 | evaluation_results = trainer.evaluate(val_data) 114 | print(f"Validation Loss: {evaluation_results['loss']}, Validation Accuracy: {evaluation_results['accuracy']}") 115 | -------------------------------------------------------------------------------- /src/ai/neural_networks.py: -------------------------------------------------------------------------------- 1 | # src/ai/neural_networks.py 2 | 3 | import tensorflow as tf 4 | from tensorflow.keras import layers, models, regularizers 5 | from tensorflow.keras.applications import VGG16, ResNet50 6 | from tensorflow.keras.utils import plot_model 7 | 8 | class CustomDenseLayer(layers.Layer): 9 | def __init__(self, units, activation='relu', kernel_regularizer=None, **kwargs): 10 | super(CustomDenseLayer, self).__init__(**kwargs) 11 | self.units = units 12 | self.activation = activation 13 | self.kernel_regularizer = regularizers.get(kernel_regularizer) 14 | 15 | def build(self, input_shape): 16 | self.w = self.add_weight(shape=(input_shape[-1], self.units), 17 | initializer='random_normal', 18 | regularizer=self.kernel_regularizer, 19 | trainable=True) 20 | self.b = self.add_weight(shape=(self.units,), 21 | initializer='zeros', 22 | trainable=True) 23 | 24 | def call(self, inputs): 25 | z = tf.matmul(inputs, self.w) + self.b 26 | return tf.keras.activations.get(self.activation)(z) 27 | 28 | class FeedforwardNN: 29 | def __init__(self, input_shape, num_classes): 30 | self.model = self.build_model(input_shape, num_classes) 31 | 32 | def build_model(self, input_shape, num_classes): 33 | model = models.Sequential() 34 | model.add(layers.Input(shape=input_shape)) 35 | model.add(CustomDenseLayer(128, activation='relu', kernel_regularizer='l2')) 36 | model.add(CustomDenseLayer(64, activation='relu')) 37 | model.add(layers.Dense(num_classes, activation='softmax')) 38 | return model 39 | 40 | def compile_model(self, learning_rate=0.001): 41 | self.model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate), 42 | loss='sparse_categorical_crossentropy', 43 | metrics=['accuracy']) 44 | 45 | def visualize_model(self, filename='model.png'): 46 | plot_model(self.model, to_file=filename, show_shapes=True, show_layer_names=True) 47 | 48 | class ConvolutionalNN: 49 | def __init__(self, input_shape, num_classes): 50 | self.model = self.build_model(input_shape, num_classes) 51 | 52 | def build_model(self, input_shape, num_classes): 53 | model = models.Sequential() 54 | model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape)) 55 | model.add(layers.MaxPooling2D((2, 2))) 56 | model.add(layers.Conv2D(64, (3, 3), activation='relu')) 57 | model.add(layers.MaxPooling2D((2, 2))) 58 | model.add(layers.Flatten()) 59 | model.add(layers.Dense(64, activation='relu')) 60 | model.add(layers.Dense(num_classes, activation='softmax')) 61 | return model 62 | 63 | def compile_model(self, learning_rate=0.001): 64 | self.model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate), 65 | loss='sparse_categorical_crossentropy', 66 | metrics=['accuracy']) 67 | 68 | def visualize_model(self, filename='cnn_model.png'): 69 | plot_model(self.model, to_file=filename, show_shapes=True, show_layer_names=True) 70 | 71 | class TransferLearningModel: 72 | def __init__(self, base_model_name, num_classes, input_shape=(224, 224, 3)): 73 | self.model = self.build_model(base_model_name, num_classes, input_shape) 74 | 75 | def build_model(self, base_model_name, num_classes, input_shape): 76 | if base_model_name == 'VGG16': 77 | base_model = VGG16(weights='imagenet', include_top=False, input_shape=input_shape) 78 | elif base_model_name == 'ResNet50': 79 | base_model = ResNet50(weights='imagenet', include_top=False, input_shape=input_shape) 80 | else: 81 | raise ValueError("Unsupported base model. Choose 'VGG16' or 'ResNet50'.") 82 | 83 | model = models.Sequential() 84 | model.add(base_model) 85 | model.add(layers.Flatten()) 86 | model.add(layers.Dense(256, activation='relu')) 87 | model.add(layers.Dense(num_classes, activation='softmax')) 88 | 89 | # Freeze the base model 90 | base_model.trainable = False 91 | return model 92 | 93 | def compile_model(self, learning_rate=0.001): 94 | self.model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate), 95 | loss='sparse_categorical_crossentropy', 96 | metrics=['accuracy']) 97 | 98 | def unfreeze_base_model(self): 99 | for layer in self.model.layers[0].layers: 100 | layer.trainable = True 101 | 102 | def visualize_model(self, filename='transfer_model.png'): 103 | plot_model(self.model, to_file =filename, show_shapes=True, show_layer_names=True) 104 | -------------------------------------------------------------------------------- /src/data/data_loader.py: -------------------------------------------------------------------------------- 1 | # data/data_loader.py 2 | 3 | import pandas as pd 4 | import os 5 | import json 6 | import numpy as np 7 | from typing import Union, List, Dict 8 | 9 | class DataLoader: 10 | def __init__(self, data_dir: str): 11 | """ 12 | Initialize the DataLoader with the directory containing the data files. 13 | :param data_dir: Directory where data files are stored. 14 | """ 15 | self.data_dir = data_dir 16 | 17 | def load_csv(self, filename: str) -> pd.DataFrame: 18 | """ 19 | Load a CSV file into a DataFrame. 20 | :param filename: Name of the CSV file. 21 | :return: DataFrame containing the data. 22 | """ 23 | file_path = os.path.join(self.data_dir, filename) 24 | if os.path.exists(file_path): 25 | return pd.read_csv(file_path) 26 | else: 27 | raise FileNotFoundError(f"{filename} not found in {self.data_dir}") 28 | 29 | def load_json(self, filename: str) -> Union[Dict, List]: 30 | """ 31 | Load a JSON file into a Python dictionary or list. 32 | :param filename: Name of the JSON file. 33 | :return: Dictionary or list containing the data. 34 | """ 35 | file_path = os.path.join(self.data_dir, filename) 36 | if os.path.exists(file_path): 37 | with open(file_path, 'r') as f: 38 | return json.load(f) 39 | else: 40 | raise FileNotFoundError(f"{filename} not found in {self.data_dir}") 41 | 42 | def load_excel(self, filename: str, sheet_name: str = None) -> pd.DataFrame: 43 | """ 44 | Load an Excel file into a DataFrame. 45 | :param filename: Name of the Excel file. 46 | :param sheet_name: Name of the sheet to load (default is the first sheet). 47 | :return: DataFrame containing the data. 48 | """ 49 | file_path = os.path.join(self.data_dir, filename) 50 | if os.path.exists(file_path): 51 | return pd.read_excel(file_path, sheet_name=sheet_name) 52 | else: 53 | raise FileNotFoundError(f"{filename} not found in {self.data_dir}") 54 | 55 | def load_images(self, image_dir: str) -> List[str]: 56 | """ 57 | Load image file paths from a directory. 58 | :param image_dir: Directory containing images. 59 | :return: List of image file paths. 60 | """ 61 | image_paths = [os.path.join(image_dir, img) for img in os.listdir(image_dir) if img.endswith(('.png', '.jpg', '.jpeg'))] 62 | if not image_paths: 63 | raise FileNotFoundError(f"No images found in {image_dir}") 64 | return image_paths 65 | 66 | def load_data(self, filename: str, file_type: str = 'csv') -> Union[pd.DataFrame, Dict, List]: 67 | """ 68 | Load data from various formats based on file extension. 69 | :param filename: Name of the file. 70 | :param file_type: Type of the file ('csv', 'json', 'excel'). 71 | :return: DataFrame, dictionary, or list containing the data. 72 | """ 73 | if file_type == 'csv': 74 | return self.load_csv(filename) 75 | elif file_type == 'json': 76 | return self.load_json(filename) 77 | elif file_type == 'excel': 78 | return self.load_excel(filename) 79 | else: 80 | raise ValueError("Unsupported file format. Please provide a CSV, JSON, or Excel file.") 81 | 82 | def validate_data(self, df: pd.DataFrame) -> Dict[str, Union[int, float]]: 83 | """ 84 | Validate the DataFrame for missing values and duplicates. 85 | :param df: DataFrame to validate. 86 | :return: Dictionary with validation results. 87 | """ 88 | missing_values = df.isnull().sum().sum() 89 | duplicate_rows = df.duplicated().sum() 90 | total_rows = df.shape[0] 91 | return { 92 | 'total_rows': total_rows, 93 | 'missing_values': missing_values, 94 | 'duplicate_rows': duplicate_rows 95 | } 96 | 97 | def explore_data(self, df: pd.DataFrame) -> None: 98 | """ 99 | Print basic statistics and information about the DataFrame. 100 | :param df: DataFrame to explore. 101 | """ 102 | print("DataFrame Info:") 103 | print(df.info()) 104 | print("\nBasic Statistics:") 105 | print(df.describe(include='all')) 106 | 107 | # Example usage 108 | if __name__ == "__main__": 109 | data_loader = DataLoader(data_dir='path/to/data') 110 | 111 | # Load a CSV file 112 | try: 113 | df = data_loader.load_data('data.csv', file_type='csv') 114 | print("CSV Data Loaded Successfully.") 115 | except Exception as e: 116 | print(e) 117 | 118 | # Validate the loaded data 119 | validation_results = data_loader.validate_data(df) 120 | print("Validation Results:") 121 | print(validation_results) 122 | 123 | # Explore the loaded data 124 | data_loader.explore_data(df) 125 | -------------------------------------------------------------------------------- /src/communication/secure_transmission.py: -------------------------------------------------------------------------------- 1 | # communication/secure_transmission.py 2 | 3 | from Crypto.Cipher import AES 4 | from Crypto.Random import get_random_bytes 5 | from Crypto.Util.Padding import pad, unpad 6 | from Crypto.PublicKey import RSA 7 | from Crypto.Signature import pkcs1_15 8 | from Crypto.Hash import SHA256 9 | import base64 10 | 11 | class SecureTransmission: 12 | def __init__(self): 13 | """Initialize the Secure Transmission class.""" 14 | self.aes_key = get_random_bytes(16) # AES key must be either 16, 24, or 32 bytes long 15 | self.rsa_key = RSA.generate(2048) # Generate RSA key pair 16 | 17 | def encrypt_aes(self, plaintext: str) -> str: 18 | """ 19 | Encrypt the plaintext using AES encryption. 20 | :param plaintext: The plaintext to encrypt. 21 | :return: Base64 encoded ciphertext. 22 | """ 23 | cipher = AES.new(self.aes_key, AES.MODE_CBC) 24 | ct_bytes = cipher.encrypt(pad(plaintext.encode(), AES.block_size)) 25 | iv = base64.b64encode(cipher.iv).decode('utf-8') 26 | ct = base64.b64encode(ct_bytes).decode('utf-8') 27 | return iv + ":" + ct 28 | 29 | def decrypt_aes(self, ciphertext: str) -> str: 30 | """ 31 | Decrypt the ciphertext using AES decryption. 32 | :param ciphertext: The Base64 encoded ciphertext to decrypt. 33 | :return: Decrypted plaintext. 34 | """ 35 | iv, ct = ciphertext.split(":") 36 | iv = base64.b64decode(iv) 37 | ct = base64.b64decode(ct) 38 | cipher = AES.new(self.aes_key, AES.MODE_CBC, iv) 39 | plaintext = unpad(cipher.decrypt(ct), AES.block_size).decode('utf-8') 40 | return plaintext 41 | 42 | def generate_rsa_keys(self) -> Tuple[bytes, bytes]: 43 | """Generate RSA public and private keys.""" 44 | private_key = self.rsa_key.export_key() 45 | public_key = self.rsa_key.publickey().export_key() 46 | return private_key, public_key 47 | 48 | def encrypt_rsa(self, message: str, public_key: bytes) -> str: 49 | """ 50 | Encrypt a message using RSA encryption. 51 | :param message: The message to encrypt. 52 | :param public_key: The recipient's public key. 53 | :return: Base64 encoded ciphertext. 54 | """ 55 | rsa_key = RSA.import_key(public_key) 56 | ciphertext = rsa_key.encrypt(message.encode(), None)[0] 57 | return base64.b64encode(ciphertext).decode('utf-8') 58 | 59 | def decrypt_rsa(self, ciphertext: str) -> str: 60 | """ 61 | Decrypt a message using RSA decryption. 62 | :param ciphertext: The Base64 encoded ciphertext to decrypt. 63 | :return: Decrypted message. 64 | """ 65 | ciphertext = base64.b64decode(ciphertext) 66 | plaintext = self.rsa_key.decrypt(ciphertext).decode('utf-8') 67 | return plaintext 68 | 69 | def sign_message(self, message: str) -> str: 70 | """ 71 | Sign a message using RSA private key. 72 | :param message: The message to sign. 73 | :return: Base64 encoded signature. 74 | """ 75 | message_hash = SHA256.new(message.encode()) 76 | signature = pkcs1_15.new(self.rsa_key).sign(message_hash) 77 | return base64.b64encode(signature).decode('utf-8') 78 | 79 | def verify_signature(self, message: str, signature: str, public_key: bytes) -> bool: 80 | """ 81 | Verify a message signature using RSA public key. 82 | :param message: The original message. 83 | :param signature: The Base64 encoded signature to verify. 84 | :param public_key: The public key to verify against. 85 | :return: True if the signature is valid, False otherwise. 86 | """ 87 | message_hash = SHA256.new(message.encode()) 88 | rsa_key = RSA.import_key(public_key) 89 | try: 90 | pkcs1_15.new(rsa_key).verify(message_hash, base64.b64decode(signature)) 91 | return True 92 | except (ValueError, TypeError): 93 | return False 94 | 95 | # Example usage 96 | if __name__ == "__main__": 97 | secure_transmission = SecureTransmission() 98 | 99 | # AES Encryption/Decryption 100 | message_aes = "This is a secret message for AES." 101 | encrypted_message_aes = secure_transmission.encrypt_aes(message_aes) 102 | print("Encrypted AES Message:", encrypted_message_aes) 103 | 104 | decrypted_message_aes = secure_transmission.decrypt_aes(encrypted_message_aes) 105 | print("Decrypted AES Message:", decrypted_message_aes) 106 | 107 | # RSA Key Generation 108 | private_key, public_key = secure_transmission.generate_rsa _keys() 109 | print("Private Key:", private_key.decode('utf-8')) 110 | print("Public Key:", public_key.decode('utf-8')) 111 | 112 | # RSA Encryption/Decryption 113 | message_rsa = "This is a secret message for RSA." 114 | encrypted_message_rsa = secure_transmission.encrypt_rsa(message_rsa, public_key) 115 | print("Encrypted RSA Message:", encrypted_message_rsa) 116 | 117 | decrypted_message_rsa = secure_transmission.decrypt_rsa(encrypted_message_rsa) 118 | print("Decrypted RSA Message:", decrypted_message_rsa) 119 | 120 | # Digital Signature 121 | message_signature = "This is a message to sign." 122 | signature = secure_transmission.sign_message(message_signature) 123 | print("Signature:", signature) 124 | 125 | is_valid = secure_transmission.verify_signature(message_signature, signature, public_key) 126 | print("Signature is valid:", is_valid) 127 | -------------------------------------------------------------------------------- /src/data/data_preprocessing.py: -------------------------------------------------------------------------------- 1 | # data/data_preprocessing.py 2 | 3 | import pandas as pd 4 | import numpy as np 5 | from sklearn.model_selection import train_test_split 6 | from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder 7 | from sklearn.impute import SimpleImputer 8 | from typing import Tuple, List, Dict 9 | 10 | class DataPreprocessor: 11 | def __init__(self, df: pd.DataFrame): 12 | """ 13 | Initialize the DataPreprocessor with a DataFrame. 14 | :param df: DataFrame to preprocess. 15 | """ 16 | self.df = df 17 | 18 | def clean_data(self) -> pd.DataFrame: 19 | """ 20 | Clean the DataFrame by handling missing values and duplicates. 21 | :return: Cleaned DataFrame. 22 | """ 23 | # Remove duplicates 24 | self.df.drop_duplicates(inplace=True) 25 | 26 | # Handle missing values 27 | imputer = SimpleImputer(strategy='mean') 28 | for column in self.df.select_dtypes(include=[np.number]).columns: 29 | self.df[column] = imputer.fit_transform(self.df[[column]]) 30 | 31 | # For categorical columns, fill missing values with the mode 32 | for column in self.df.select_dtypes(include=[object]).columns: 33 | self.df[column].fillna(self.df[column].mode()[0], inplace=True) 34 | 35 | return self.df 36 | 37 | def encode_labels(self, column: str) -> pd.DataFrame: 38 | """ 39 | Encode categorical labels into numerical values. 40 | :param column: Column name to encode. 41 | :return: DataFrame with encoded labels. 42 | """ 43 | le = LabelEncoder() 44 | self.df[column] = le.fit_transform(self.df[column]) 45 | return self.df 46 | 47 | def one_hot_encode(self, columns: List[str]) -> pd.DataFrame: 48 | """ 49 | One-hot encode specified categorical columns. 50 | :param columns: List of column names to one-hot encode. 51 | :return: DataFrame with one-hot encoded columns. 52 | """ 53 | self.df = pd.get_dummies(self.df, columns=columns, drop_first=True) 54 | return self.df 55 | 56 | def split_data(self, target_column: str, test_size: float = 0.2, random_state: int = 42) -> Tuple[pd.DataFrame, pd.DataFrame, pd.Series, pd.Series]: 57 | """ 58 | Split the DataFrame into training and testing sets. 59 | :param target_column: Name of the target column. 60 | :param test_size: Proportion of the dataset to include in the test split. 61 | :param random_state: Random seed for reproducibility. 62 | :return: X_train, X_test, y_train, y_test. 63 | """ 64 | X = self.df.drop(columns=[target_column]) 65 | y = self.df[target_column] 66 | return train_test_split(X, y, test_size=test_size, random_state=random_state) 67 | 68 | def scale_features(self, feature_columns: List[str]) -> pd.DataFrame: 69 | """ 70 | Scale features using StandardScaler. 71 | :param feature_columns: List of feature column names to scale. 72 | :return: DataFrame with scaled features. 73 | """ 74 | scaler = StandardScaler() 75 | self.df[feature_columns] = scaler.fit_transform(self.df[feature_columns]) 76 | return self.df 77 | 78 | def feature_engineering(self, new_column_name: str, operation: str, columns: List[str]) -> pd.DataFrame: 79 | """ 80 | Create a new feature based on existing columns. 81 | :param new_column_name: Name of the new column. 82 | :param operation: Operation to perform ('sum', 'mean', 'product'). 83 | :param columns: List of columns to use for the operation. 84 | :return: DataFrame with the new feature. 85 | """ 86 | if operation == 'sum': 87 | self.df[new_column_name] = self.df[columns].sum(axis=1) 88 | elif operation == 'mean': 89 | self.df[new_column_name] = self.df[columns].mean(axis=1) 90 | elif operation == 'product': 91 | self.df[new_column_name] = self.df[columns].prod(axis=1) 92 | else: 93 | raise ValueError("Unsupported operation. Use 'sum', 'mean', or 'product'.") 94 | return self.df 95 | 96 | def remove_outliers(self, column: str, threshold: float = 1.5) -> pd.DataFrame: 97 | """ 98 | Remove outliers from a specified column using the IQR method. 99 | :param column: Column name to check for outliers. 100 | :param threshold: IQR multiplier to define outliers. 101 | :return: DataFrame with outliers removed. 102 | """ 103 | Q1 = self.df[column].quantile(0.25) 104 | Q3 = self.df[column].quantile(0.75) 105 | IQR = Q3 - Q1 106 | self.df = self.df[~((self.df[column] < (Q1 - threshold * IQR)) | (self.df[column] > (Q3 + threshold * IQR)))] 107 | return self.df 108 | 109 | # Example usage 110 | if __name__ == "__main__": 111 | # Load data 112 | data_loader = DataLoader(data_dir='path/to/data') 113 | df = data_loader.load_data('data.csv', file_type='csv') 114 | 115 | # Preprocess data 116 | preprocessor = DataPreprocessor(df) 117 | df = preprocessor.clean_data() 118 | df = preprocessor.encode_labels('target_column') 119 | df = preprocessor.one_hot_encode(['category_column1', 'category_column2']) 120 | X_train, X_test, y_train, y_test = preprocessor.split_data('target_column') 121 | X_train = preprocessor.scale_features(['feature_column1', 'feature_column2']) 122 | X_train = preprocessor.feature_engineering('new_column', 'sum', ['column1', 'column2']) 123 | X_train = preprocessor.remove_outliers('column_with_outliers') 124 | -------------------------------------------------------------------------------- /src/quantum/quantum_utils.py: -------------------------------------------------------------------------------- 1 | from typing import List, Tuple, Optional, Dict, Union, Callable 2 | import numpy as np 3 | from scipy.linalg import expm 4 | from qiskit import QuantumCircuit, Aero 5 | from qiskit.quantum_info import Statevector, DensityMatrix, Operator, state_fidelity 6 | from qiskit.transpiler import PassManager 7 | from qiskit.transpiler.passes import ( 8 | Optimize1QGates, CXCancellation, CommutativeCancellation, 9 | OptimizeSwapBeforeMeasure, Unroller, Depth, FixedPoint 10 | ) 11 | from qiskit.providers.aer.noise import NoiseModel 12 | import torch 13 | import tensorflow as tf 14 | from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor 15 | import logging 16 | from dataclasses import dataclass 17 | 18 | # Configure logging 19 | logging.basicConfig(level=logging.INFO) 20 | logger = logging.getLogger(__name__) 21 | 22 | @dataclass 23 | class QuantumState: 24 | """Dataclass for quantum state information.""" 25 | statevector: np.ndarray 26 | density_matrix: Optional[np.ndarray] = None 27 | fidelity: Optional[float] = None 28 | entanglement_entropy: Optional[float] = None 29 | 30 | class QuantumNoiseHandler: 31 | """Advanced quantum noise handling and mitigation.""" 32 | 33 | def __init__(self, noise_model_type: str = 'default'): 34 | self.noise_model = self._initialize_noise_model(noise_model_type) 35 | self.error_rates = self._calculate_error_rates() 36 | 37 | def _initialize_noise_model(self, model_type: str) -> NoiseModel: 38 | """Initialize custom noise model.""" 39 | noise_model = NoiseModel() 40 | 41 | if model_type == 'default': 42 | # Add basic decoherence noise 43 | noise_model.add_all_qubit_quantum_error( 44 | quantum_error_channel('depolarizing', probability=0.001), 45 | ['u1', 'u2', 'u3'] 46 | ) 47 | elif model_type == 'advanced': 48 | # Add sophisticated noise channels 49 | noise_model.add_all_qubit_quantum_error( 50 | quantum_error_channel('amplitude_damping', gamma=0.001), 51 | ['u1', 'u2', 'u3'] 52 | ) 53 | noise_model.add_all_qubit_quantum_error( 54 | quantum_error_channel('phase_damping', lambda_param=0.001), 55 | ['cx'] 56 | ) 57 | 58 | return noise_model 59 | 60 | def _calculate_error_rates(self) -> Dict[str, float]: 61 | """Calculate error rates for different quantum operations.""" 62 | return { 63 | 'single_qubit': 0.001, 64 | 'two_qubit': 0.01, 65 | 'measurement': 0.02 66 | } 67 | 68 | def apply_error_correction(self, circuit: QuantumCircuit) -> QuantumCircuit: 69 | """Apply quantum error correction codes.""" 70 | # Implement surface code or other error correction schemes 71 | corrected_circuit = self._apply_surface_code(circuit) 72 | return corrected_circuit 73 | 74 | def _apply_surface_code(self, circuit: QuantumCircuit) -> QuantumCircuit: 75 | """Apply surface code error correction.""" 76 | # Implementation of surface code 77 | pass 78 | 79 | class QuantumOptimizer: 80 | """Advanced quantum circuit optimization techniques.""" 81 | 82 | def __init__(self, optimization_level: int = 3): 83 | self.optimization_level = optimization_level 84 | self.pass_manager = self._create_pass_manager() 85 | 86 | def _create_pass_manager(self) -> PassManager: 87 | """Create an advanced pass manager for circuit optimization.""" 88 | passes = [ 89 | Optimize1QGates(), 90 | CXCancellation(), 91 | CommutativeCancellation(), 92 | OptimizeSwapBeforeMeasure(), 93 | Unroller(), 94 | Depth(), 95 | FixedPoint(max_iterations=20) 96 | ] 97 | 98 | if self.optimization_level >= 2: 99 | passes.extend([ 100 | # Add more sophisticated optimization passes 101 | self._custom_optimization_pass(), 102 | self._quantum_topology_optimization() 103 | ]) 104 | 105 | return PassManager(passes) 106 | 107 | def _custom_optimization_pass(self): 108 | """Custom optimization pass for specific quantum architectures.""" 109 | pass 110 | 111 | def _quantum_topology_optimization(self): 112 | """Optimize quantum circuit based on hardware topology.""" 113 | pass 114 | 115 | def optimize_circuit(self, circuit: QuantumCircuit) -> QuantumCircuit: 116 | """Optimize quantum circuit using advanced techniques.""" 117 | return self.pass_manager.run(circuit) 118 | 119 | class QuantumMetrics: 120 | """Advanced quantum metrics and measurements.""" 121 | 122 | @staticmethod 123 | def calculate_entanglement_entropy(density_matrix: np.ndarray) -> float: 124 | """Calculate the von Neumann entropy of entanglement.""" 125 | eigenvalues = np.linalg.eigvals(density_matrix) 126 | eigenvalues = eigenvalues[eigenvalues > 0] # Remove zero eigenvalues 127 | return -np.sum(eigenvalues * np.log2(eigenvalues)) 128 | 129 | @staticmethod 130 | def calculate_quantum_fisher_information( 131 | state: QuantumState, 132 | parameter: float, 133 | generator: np.ndarray 134 | ) -> float: 135 | """Calculate the quantum Fisher information.""" 136 | return 4 * ( 137 | np.trace(state.density_matrix @ generator @ generator) - 138 | np.trace(state.density_matrix @ generator)**2 139 | ) 140 | 141 | @staticmethod 142 | def calculate_quantum_discord( 143 | state: QuantumState, 144 | subsystem_dims: Tuple[int, int] 145 | ) -> float: 146 | """Calculate quantum discord between subsystems.""" 147 | # Implementation of quantum discord calculation 148 | pass 149 | 150 | class QuantumParallelProcessor: 151 | """Parallel processing for quantum computations.""" 152 | 153 | def __init__(self, max_workers: int = None): 154 | self.max_workers = max_workers 155 | 156 | def parallel_circuit_execution( 157 | self, 158 | circuits: List[QuantumCircuit], 159 | backend: str = 'qasm_simulator' 160 | ) -> List[Dict]: 161 | """Execute quantum circuits in parallel.""" 162 | with ProcessPoolExecutor(max_workers=self.max_workers) as executor: 163 | results = list(executor.map( 164 | lambda circuit: self._execute_single_circuit(circuit, backend), 165 | circuits 166 | )) 167 | return results 168 | 169 | @staticmethod 170 | def _execute_single_circuit( 171 | circuit: QuantumCircuit, 172 | backend: str 173 | ) -> Dict: 174 | """Execute a single quantum circuit.""" 175 | try: 176 | result = execute(circuit, Aer.get_backend(backend)).result() 177 | return {'success': True, 'counts': result.get_counts()} 178 | except Exception as e: 179 | logger.error(f"Circuit execution failed: {str(e)}") 180 | return {'success': False, 'error': str(e)} 181 | 182 | class QuantumTensorNetwork: 183 | """Quantum tensor network implementation.""" 184 | 185 | def __init__(self, num_qubits: int, bond_dimension: int): 186 | self.num_qubits = num_qubits 187 | self.bond_dimension = bond_dimension 188 | self.tensors = self._initialize_tensors() 189 | 190 | def _initialize_tensors(self) -> List[np.ndarray]: 191 | """Initialize quantum tensor network.""" 192 | return [ 193 | np.random.random((2, self.bond_dimension, self.bond_dimension)) 194 | for _ in range(self.num_qubits) 195 | ] 196 | 197 | def contract_network(self) -> np.ndarray: 198 | """Contract the quantum tensor network.""" 199 | # Implementation of tensor network contraction 200 | pass 201 | 202 | def quantum_state_tomography( 203 | measurements: List[Dict[str, int]], 204 | bases: List[str] 205 | ) -> QuantumState: 206 | """Perform quantum state tomography.""" 207 | # Implementation of quantum state tomography 208 | pass 209 | 210 | def quantum_process_tomography( 211 | input_states: List[QuantumState], 212 | output_states: List[QuantumState ] 213 | ) -> np.ndarray: 214 | """Perform quantum process tomography.""" 215 | # Implementation of quantum process tomography 216 | pass 217 | 218 | def quantum_error_channel( 219 | error_type: str, 220 | probability: float, 221 | **kwargs 222 | ) -> np.ndarray: 223 | """Generate a quantum error channel.""" 224 | # Implementation of quantum error channel generation 225 | pass 226 | 227 | def validate_circuit(circuit: QuantumCircuit) -> None: 228 | """Validate the quantum circuit.""" 229 | if not circuit: 230 | raise ValueError("Circuit is empty") 231 | 232 | def optimize_circuit(circuit: QuantumCircuit) -> QuantumCircuit: 233 | """Optimize the quantum circuit.""" 234 | optimizer = QuantumOptimizer() 235 | return optimizer.optimize_circuit(circuit) 236 | 237 | def measure_state_fidelity(statevector: Statevector, target_state: str) -> float: 238 | """Measure the fidelity of the statevector with respect to the target state.""" 239 | target_statevector = Statevector.from_label(target_state) 240 | return state_fidelity(statevector, target_statevector) 241 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | [![Certified Information Systems Security Professional (CISSP)](https://img.shields.io/badge/CISSP-Certified-0072C6?style=flat&logo=isc2)](https://www.isc2.org/Certifications/CISSP) 2 | [![Certified Ethical Hacker (CEH)](https://img.shields.io/badge/CEH-Certified-0072C6?style=flat&logo=ec-council)](https://www.eccouncil.org/programs/certified-ethical-hacker-ceh/) 3 | [![Certified Information Security Manager (CISM)](https://img.shields.io/badge/CISM-Certified-0072C6?style=flat&logo=isaca)](https://www.isaca.org/credentialing/cism) 4 | [![Certified Data Privacy Solutions Engineer (CDPSE)](https://img.shields.io/badge/CDPSE-Certified-0072C6?style=flat&logo=isaca)](https://www.isaca.org/credentialing/cdpse) 5 | [![AWS Certified 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Quantum Celestia Nexus by KOSASIH is licensed under Creative Commons Attribution 4.0 International

180 | 181 | # quantum-celestia-nexus 182 | The core framework of the Quantum Celestia Nexus project, this repository contains the foundational algorithms, quantum computing architectures, and AI models that drive the integration of quantum technologies with advanced artificial intelligence. It serves as the backbone for data processing, real-time learning, and collaborative research, enabling groundbreaking solutions to complex challenges across various scientific domains. 183 | 184 | # Quantum Celestia Nexus 185 | 186 | ## Project Overview 187 | Quantum Celestia Nexus is an ambitious initiative aimed at creating a fully integrated quantum computing and artificial intelligence ecosystem. This project leverages the principles of quantum mechanics to develop advanced AI algorithms capable of solving complex problems across various scientific domains, including astrophysics, genomics, climate modeling, and materials science. 188 | 189 | ## Features 190 | - Quantum circuit design and simulation 191 | - Advanced neural network architectures 192 | - Real-time data processing and analysis 193 | - Secure quantum communication protocols 194 | - Comprehensive documentation and tutorials 195 | 196 | ## Installation Instructions 197 | 198 | ### Prerequisites 199 | - Python 3.7 or higher 200 | - pip (Python package installer) 201 | - Git 202 | 203 | ### Clone the Repository 204 | 205 | ```bash 206 | 1 git clone https://github.com/KOSASIH/quantum-celestia-nexus-core.git 207 | 2 cd quantum-celestia-nexus-core 208 | ``` 209 | 210 | ### Install Dependencies 211 | 212 | ```bash 213 | 1 pip install -r requirements.txt 214 | ``` 215 | 216 | ## Usage Guidelines 217 | 218 | ### Running the Main Application 219 | 220 | To run the main application, execute the following command: 221 | 222 | ```bash 223 | 1 python src/main.py 224 | ``` 225 | 226 | ### Running Tests 227 | 228 | To run the unit tests, use: 229 | 230 | ```bash 231 | 1 pytest tests/ 232 | ``` 233 | 234 | # Example Usage 235 | 236 | Refer to the examples/ directory for example scripts demonstrating how to use various components of the project. 237 | 238 | # Contributing 239 | 240 | Contributions are welcome! Please read the [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines on how to contribute to this project. 241 | 242 | # License 243 | 244 | This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details. 245 | --------------------------------------------------------------------------------