├── .gitignore ├── images └── universal_quantum_network.jpeg ├── .github └── ISSUE_TEMPLATE │ ├── custom.md │ ├── feature_request.md │ └── bug_report.md ├── examples ├── demo │ ├── demo_entanglement.py │ ├── demo_security.py │ ├── demo_ai_integration.py │ └── demo_network_performance.py ├── real_time_data_processing.py ├── basic_example.py └── advanced_example.py ├── scripts ├── backup_data.sh ├── setup_env.sh ├── update_dependencies.sh ├── deploy.sh └── run_simulations.sh ├── tests ├── integration_tests │ ├── test_ai_integration.py │ ├── test_security.py │ ├── test_quantum_comm.py │ └── test_network.py └── unit_tests │ ├── test_error_handling.py │ ├── test_quality_of_service.py │ ├── test_entanglement.py │ ├── test_neural_networks.py │ ├── test_intrusion_detection.py │ ├── test_ai_optimizer.py │ └── test_routing.py ├── src ├── ai_integration │ ├── natural_language_processing │ │ ├── nlp_engine.py │ │ └── chatbot.py │ ├── machine_learning_models │ │ ├── neural_networks │ │ │ ├── recurrent_network.py │ │ │ ├── transformer_model.py │ │ │ └── convolutional_network.py │ │ ├── clustering.py │ │ ├── anomaly_detection.py │ │ ├── predictive_model.py │ │ └── reinforcement_learning │ │ │ ├── environment.py │ │ │ └── rl_agent.py │ ├── cognitive_computing │ │ ├── reasoning_engine.py │ │ └── knowledge_graph.py │ ├── ai_optimizer.py │ └── security_ai.py ├── network_protocols │ ├── quality_of_service.py │ ├── error_handling.py │ ├── network_virtualization.py │ ├── cross_layer_optimization.py │ ├── network_topology.py │ ├── adaptive_routing.py │ ├── multicast_protocol.py │ ├── congestion_control.py │ └── routing.py ├── utils │ ├── visualization.py │ ├── config.py │ ├── benchmarking.py │ ├── logger.py │ ├── performance_metrics.py │ └── data_storage.py ├── security │ ├── security_audit.py │ ├── secure_messaging.py │ ├── intrusion_detection.py │ ├── threat_modeling.py │ └── cryptographic_protocols.py ├── simulations │ ├── scenario_generator.py │ ├── fault_tolerance_simulation.py │ ├── scalability_tests.py │ ├── quantum_simulator.py │ └── network_simulator.py └── quantum_comm │ ├── quantum_fidelity.py │ ├── quantum_key_distribution.py │ ├── quantum_measurement.py │ ├── superposition.py │ ├── quantum_channel.py │ ├── quantum_network_topology.py │ ├── quantum_state_preparation.py │ ├── quantum_cryptography.py │ ├── entanglement.py │ ├── quantum_error_correction.py │ ├── teleportation.py │ └── quantum_teleportation_network.py ├── LICENSE ├── docs ├── tutorials │ ├── advanced_usage.md │ └── getting_started.md ├── research_papers │ └── relevant_papers.md ├── protocols.md ├── architecture.md ├── case_studies │ └── real_world_applications.md ├── technologies.md └── code_structure.md ├── CONTRIBUTING.md └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | # Google App Engine generated folder 2 | appengine-generated/ 3 | -------------------------------------------------------------------------------- /images/universal_quantum_network.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KOSASIH/UniversalQuantumNetwork/HEAD/images/universal_quantum_network.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 | -------------------------------------------------------------------------------- /examples/demo/demo_entanglement.py: -------------------------------------------------------------------------------- 1 | from src.quantum_comm.entanglement import EntanglementProtocol 2 | 3 | def main(): 4 | # Demo for entanglement communication 5 | protocol = EntanglementProtocol() 6 | entangled_state = protocol.entangled_state() 7 | print("Demo Entanglement: Entangled State:", entangled_state) 8 | 9 | if __name__ == "__main__": 10 | main() 11 | -------------------------------------------------------------------------------- /examples/demo/demo_security.py: -------------------------------------------------------------------------------- 1 | from src.security.intrusion_detection import IntrusionDetectionSystem 2 | 3 | def main(): 4 | # Demo for security features 5 | ids = IntrusionDetectionSystem() 6 | traffic_data = [0.5, 0.7, 0.9, 0.2, 0.1] # Simulated traffic data 7 | for data in traffic_data: 8 | ids.detect_intrusion(data) 9 | 10 | if __name__ == "__main__": 11 | main() 12 | -------------------------------------------------------------------------------- /scripts/backup_data.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # Script for backing up data for the Universal Quantum Network (UQN) 4 | 5 | echo "Backing up data..." 6 | 7 | # Define backup directory 8 | BACKUP_DIR="backup_$(date +%Y%m%d_%H%M%S)" 9 | mkdir -p $BACKUP_DIR 10 | 11 | # Copy data files to the backup directory 12 | cp -r data/* $BACKUP_DIR/ 13 | 14 | echo "Backup completed successfully. Backup stored in $BACKUP_DIR." 15 | -------------------------------------------------------------------------------- /scripts/setup_env.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # Environment setup script for the Universal Quantum Network (UQN) 4 | 5 | echo "Setting up the environment for UQN..." 6 | 7 | # Create a virtual environment 8 | python3 -m venv venv 9 | 10 | # Activate the virtual environment 11 | source venv/bin/activate 12 | 13 | # Install required packages 14 | pip install -r requirements.txt 15 | 16 | echo "Environment setup completed successfully." 17 | -------------------------------------------------------------------------------- /scripts/update_dependencies.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # Script to update project dependencies for the Universal Quantum Network (UQN) 4 | 5 | echo "Updating project dependencies..." 6 | 7 | # Activate the virtual environment 8 | source venv/bin/activate 9 | 10 | # Update pip 11 | pip install --upgrade pip 12 | 13 | # Update all dependencies 14 | pip install -r requirements.txt --upgrade 15 | 16 | echo "Dependencies updated successfully." 17 | -------------------------------------------------------------------------------- /scripts/deploy.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # Deployment script for the Universal Quantum Network (UQN) 4 | 5 | echo "Starting deployment of the Universal Quantum Network..." 6 | 7 | # Pull the latest code from the repository 8 | git pull origin main 9 | 10 | # Install required packages 11 | pip install -r requirements.txt 12 | 13 | # Run migrations if applicable 14 | # python manage.py migrate 15 | 16 | echo "Deployment completed successfully." 17 | -------------------------------------------------------------------------------- /examples/demo/demo_ai_integration.py: -------------------------------------------------------------------------------- 1 | from src.ai_integration.ai_optimizer import AIOptimizer 2 | 3 | def main(): 4 | # Demo for AI integration 5 | def objective_function(x): 6 | return (x - 2) ** 2 7 | 8 | optimizer = AIOptimizer(objective_function) 9 | optimal_value = optimizer.optimize(initial_guess=[0]) 10 | print("Demo AI Integration: Optimal Value:", optimal_value) 11 | 12 | if __name__ == "__main__": 13 | main() 14 | -------------------------------------------------------------------------------- /tests/integration_tests/test_ai_integration.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | from src.ai_integration.ai_system import AISystem 3 | 4 | class TestAISystem(unittest.TestCase): 5 | def setUp(self): 6 | self.ai_system = AISystem() 7 | 8 | def test_ai_integration(self): 9 | result = self.ai_system.run_integration_test() 10 | self.assertTrue(result) # Expecting integration test to pass 11 | 12 | if __name__ == "__main__": 13 | unittest.main() 14 | -------------------------------------------------------------------------------- /tests/integration_tests/test_security.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | from src.security.security_manager import SecurityManager 3 | 4 | class TestSecurityManager(unittest.TestCase): 5 | def setUp(self): 6 | self.manager = SecurityManager() 7 | 8 | def test_security_features(self): 9 | result = self.manager.check_security() 10 | self.assertTrue(result) # Expecting security checks to pass 11 | 12 | if __name__ == "__main__": 13 | unittest.main() 14 | -------------------------------------------------------------------------------- /tests/unit_tests/test_error_handling.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | from src.network_protocols.error_handling import ErrorHandling 3 | 4 | class TestErrorHandling(unittest.TestCase): 5 | def setUp(self): 6 | self.error_handler = ErrorHandling() 7 | 8 | def test_log_error(self): 9 | self.error_handler.log_error("Test error") 10 | self.assertIn("Test error", self.error_handler.error_log) 11 | 12 | if __name__ == "__main__": 13 | unittest.main() 14 | -------------------------------------------------------------------------------- /tests/unit_tests/test_quality_of_service.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | from src.network_protocols.quality_of_service import QualityOfService 3 | 4 | class TestQualityOfService(unittest.TestCase): 5 | def setUp(self): 6 | self.qos = QualityOfService() 7 | 8 | def test_set_qos(self): 9 | self.qos.set_qos("bandwidth", "100Mbps") 10 | self.assertEqual(self.qos.get_qos()["bandwidth"], "100Mbps") 11 | 12 | if __name__ == "__main__": 13 | unittest.main() 14 | -------------------------------------------------------------------------------- /tests/unit_tests/test_entanglement.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | from src.quantum_comm.entanglement import EntanglementProtocol 3 | 4 | class TestEntanglementProtocol(unittest.TestCase): 5 | def setUp(self): 6 | self.protocol = EntanglementProtocol() 7 | 8 | def test_entangled_state(self): 9 | state = self.protocol.entangled_state() 10 | self.assertEqual(len(state), 4) # Expecting a 2-qubit entangled state 11 | 12 | if __name__ == "__main__": 13 | unittest.main() 14 | -------------------------------------------------------------------------------- /tests/unit_tests/test_neural_networks.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | from src.ai_integration.neural_networks.convolutional_network import ConvolutionalNetwork 3 | 4 | class TestConvolutionalNetwork(unittest.TestCase): 5 | def setUp(self): 6 | self.cnn = ConvolutionalNetwork() 7 | self.cnn.compile_model() 8 | 9 | def test_model_compilation(self): 10 | self.assertIsNotNone(self.cnn.model) # Ensure model is compiled 11 | 12 | if __name__ == "__main__": 13 | unittest.main() 14 | -------------------------------------------------------------------------------- /tests/unit_tests/test_intrusion_detection.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | from src.security.intrusion_detection import IntrusionDetectionSystem 3 | 4 | class TestIntrusionDetection(unittest.TestCase): 5 | def setUp(self): 6 | self.ids = IntrusionDetectionSystem() 7 | 8 | def test_detect_intrusion(self): 9 | result = self.ids.detect_intrusion([0.5, 0.9]) 10 | self.assertIsInstance(result, bool) # Expecting a boolean result 11 | 12 | if __name__ == "__main__": 13 | unittest.main() 14 | -------------------------------------------------------------------------------- /tests/integration_tests/test_quantum_comm.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | from src.quantum_comm.quantum_network import QuantumNetwork 3 | 4 | class TestQuantumNetwork(unittest.TestCase): 5 | def setUp(self): 6 | self.network = QuantumNetwork() 7 | 8 | def test_quantum_communication(self): 9 | result = self.network.send_message("Hello Quantum") 10 | self.assertEqual(result, "Message sent: Hello Quantum") # Check message sending 11 | 12 | if __name__ == "__main__": 13 | unittest.main() 14 | -------------------------------------------------------------------------------- /tests/unit_tests/test_ai_optimizer.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | from src.ai_integration.ai_optimizer import AIOptimizer 3 | 4 | class TestAIOptimizer(unittest.TestCase): 5 | def setUp(self): 6 | self.optimizer = AIOptimizer(lambda x: (x - 2) ** 2) 7 | 8 | def test_optimize(self): 9 | optimal_value = self.optimizer.optimize(initial_guess=[0]) 10 | self.assertAlmostEqual(optimal_value.fun, 0, places=2) # Expecting minimum at x=2 11 | 12 | if __name__ == "__main__": 13 | unittest.main() 14 | -------------------------------------------------------------------------------- /scripts/run_simulations.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # Script to run simulations for testing the Universal Quantum Network (UQN) 4 | 5 | echo "Running simulations..." 6 | 7 | # Activate the virtual environment 8 | source venv/bin/activate 9 | 10 | # Run the simulation scripts 11 | python3 src/simulations/quantum_simulator.py 12 | python3 src/simulations/network_simulator.py 13 | python3 src/simulations/scenario_generator.py 14 | python3 src/simulations/fault_tolerance_simulation.py 15 | python3 src/simulations/scalability_tests.py 16 | 17 | echo "Simulations completed successfully." 18 | -------------------------------------------------------------------------------- /examples/real_time_data_processing.py: -------------------------------------------------------------------------------- 1 | import random 2 | import time 3 | 4 | class RealTimeDataProcessor: 5 | def process_data(self, data): 6 | """Simulate real-time data processing.""" 7 | print(f"Processing data: {data}") 8 | time.sleep(1) # Simulate processing time 9 | print("Data processed.") 10 | 11 | def main(): 12 | processor = RealTimeDataProcessor() 13 | for _ in range(5): # Simulate processing 5 data points 14 | data = random.randint(1, 100) 15 | processor.process_data(data) 16 | 17 | if __name__ == "__main__": 18 | main() 19 | -------------------------------------------------------------------------------- /src/ai_integration/natural_language_processing/nlp_engine.py: -------------------------------------------------------------------------------- 1 | import spacy 2 | 3 | class NLPEngine: 4 | def __init__(self): 5 | self.nlp = spacy.load("en_core_web_sm") 6 | 7 | def process_text(self, text): 8 | """Process the input text and return the parsed document.""" 9 | doc = self.nlp(text) 10 | return doc 11 | 12 | if __name__ == "__main__": 13 | nlp_engine = NLPEngine() 14 | text = "Hello, how can I assist you today?" 15 | processed_doc = nlp_engine.process_text(text) 16 | print("Processed Text:", [(token.text, token.pos_) for token in processed_doc]) 17 | -------------------------------------------------------------------------------- /examples/demo/demo_network_performance.py: -------------------------------------------------------------------------------- 1 | from src.network_protocols.network_simulator import NetworkSimulator 2 | import numpy as np 3 | 4 | def main(): 5 | # Demo for network performance evaluation 6 | topology = np.array([[0, 1, 0, 1], 7 | [1, 0,1, 0], 8 | [0, 1, 0, 1], 9 | [1, 0, 1, 0]]) 10 | simulator = NetworkSimulator(topology) 11 | performance_metrics = simulator.evaluate_performance() 12 | print("Demo Network Performance: Performance Metrics:", performance_metrics) 13 | 14 | if __name__ == "__main__": 15 | main() 16 | -------------------------------------------------------------------------------- /src/ai_integration/machine_learning_models/neural_networks/recurrent_network.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | from tensorflow.keras import layers, models 3 | 4 | class RecurrentNetwork: 5 | def __init__(self): 6 | self.model = models.Sequential([ 7 | layers.SimpleRNN(50, input_shape=(10, 1)), 8 | layers.Dense(1) 9 | ]) 10 | 11 | def compile_model(self): 12 | """Compile the RNN model.""" 13 | self.model.compile(optimizer='adam', loss='mean_squared_error') 14 | 15 | if __name__ == "__main__": 16 | rnn = RecurrentNetwork() 17 | rnn.compile_model() 18 | print("Recurrent Network Compiled.") 19 | -------------------------------------------------------------------------------- /src/network_protocols/quality_of_service.py: -------------------------------------------------------------------------------- 1 | class QualityOfService: 2 | def __init__(self): 3 | self.qos_parameters = {} 4 | 5 | def set_qos(self, parameter, value): 6 | """Set a QoS parameter.""" 7 | self.qos_parameters[parameter] = value 8 | print(f"QoS parameter '{parameter}' set to {value}.") 9 | 10 | def get_qos(self): 11 | """Get the current QoS parameters.""" 12 | return self.qos_parameters 13 | 14 | if __name__ == "__main__": 15 | qos = QualityOfService() 16 | qos.set_qos("bandwidth", "100Mbps") 17 | qos.set_qos("latency", "20ms") 18 | print("Current QoS Parameters:", qos.get_qos()) 19 | -------------------------------------------------------------------------------- /src/ai_integration/cognitive_computing/reasoning_engine.py: -------------------------------------------------------------------------------- 1 | class ReasoningEngine: 2 | def __init__(self): 3 | self.knowledge_base = {} 4 | 5 | def add_fact(self, fact): 6 | """Add a fact to the knowledge base.""" 7 | self.knowledge_base[fact] = True 8 | 9 | def query_fact(self, fact): 10 | """Query a fact from the knowledge base.""" 11 | return self.knowledge_base.get(fact, False) 12 | 13 | if __name__ == "__main__": 14 | reasoning_engine = ReasoningEngine() 15 | reasoning_engine.add_fact("The sky is blue.") 16 | result = reasoning_engine.query_fact("The sky is blue.") 17 | print("Fact Query Result:", result) 18 | -------------------------------------------------------------------------------- /src/utils/visualization.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | 3 | class Visualization: 4 | @staticmethod 5 | def plot_data(x, y, title='Data Visualization', xlabel='X-axis', ylabel='Y-axis'): 6 | """Plot data using Matplotlib.""" 7 | plt.figure(figsize=(10, 5)) 8 | plt.plot(x, y, marker='o') 9 | plt.title(title) 10 | plt.xlabel(xlabel) 11 | plt.ylabel(ylabel) 12 | plt.grid() 13 | plt.show() 14 | 15 | if __name__ == "__main__": 16 | # Example usage 17 | x = [1, 2, 3, 4, 5] 18 | y = [2, 3, 5, 7, 11] 19 | Visualization.plot_data(x, y, title='Example Plot', xlabel='Numbers', ylabel='Prime Numbers') 20 | -------------------------------------------------------------------------------- /src/security/security_audit.py: -------------------------------------------------------------------------------- 1 | class SecurityAudit: 2 | def __init__(self): 3 | self.audit_logs = [] 4 | 5 | def log_event(self, event): 6 | """Log a security event.""" 7 | self.audit_logs.append(event) 8 | print(f"Event logged: {event}") 9 | 10 | def generate_report(self): 11 | """Generate a security audit report.""" 12 | print("Security Audit Report:") 13 | for log in self.audit_logs: 14 | print(f"- {log}") 15 | 16 | if __name__ == "__main__": 17 | audit = SecurityAudit() 18 | audit.log_event("User login attempt.") 19 | audit.log_event("File access by user.") 20 | audit.generate_report() 21 | -------------------------------------------------------------------------------- /src/simulations/scenario_generator.py: -------------------------------------------------------------------------------- 1 | import random 2 | 3 | class ScenarioGenerator: 4 | def __init__(self): 5 | self.scenarios = [] 6 | 7 | def generate_scenario(self, num_nodes, max_traffic): 8 | """Generate a random network scenario.""" 9 | scenario = { 10 | 'nodes': num_nodes, 11 | 'traffic': [random.randint(1, max_traffic) for _ in range(num_nodes)] 12 | } 13 | self.scenarios.append(scenario) 14 | return scenario 15 | 16 | if __name__ == "__main__": 17 | generator = ScenarioGenerator() 18 | scenario = generator.generate_scenario(num_nodes=5, max_traffic=100) 19 | print("Generated Scenario:", scenario) 20 | -------------------------------------------------------------------------------- /src/utils/config.py: -------------------------------------------------------------------------------- 1 | import json 2 | 3 | class Config: 4 | def __init__(self, config_file='config.json'): 5 | """Load configuration from a JSON file.""" 6 | self.config_file = config_file 7 | self.config_data = self.load_config() 8 | 9 | def load_config(self): 10 | """Load configuration data from the JSON file.""" 11 | with open(self.config_file, 'r') as file: 12 | return json.load(file) 13 | 14 | def get(self, key): 15 | """Get a configuration value by key.""" 16 | return self.config_data.get(key) 17 | 18 | if __name__ == "__main__": 19 | config = Config() 20 | print("Database URL:", config.get("database_url")) 21 | -------------------------------------------------------------------------------- /.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 | -------------------------------------------------------------------------------- /tests/unit_tests/test_routing.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | import numpy as np 3 | from src.network_protocols.routing import RoutingAlgorithm 4 | 5 | class TestRoutingAlgorithm(unittest.TestCase): 6 | def setUp(self): 7 | self.topology = np.array([[0, 1, 4, 0], 8 | [1, 0, 2, 5], 9 | [4, 2, 0, 1], 10 | [0, 5, 1, 0]]) 11 | self.routing = RoutingAlgorithm(self.topology) 12 | 13 | def test_dijkstra(self): 14 | distance = self.routing.dijkstra(0, 3) 15 | self.assertGreaterEqual(distance, 0) # Distance should be non-negative 16 | 17 | if __name__ == "__main__": 18 | unittest.main() 19 | -------------------------------------------------------------------------------- /src/quantum_comm/quantum_fidelity.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from qiskit import QuantumCircuit, Aer, execute 3 | 4 | class QuantumFidelity: 5 | def __init__(self): 6 | self.backend = Aer.get_backend('statevector_simulator') 7 | 8 | def calculate_fidelity(self, state1, state2): 9 | """Calculate the fidelity between two quantum states.""" 10 | fidelity = np.abs(np.dot(state1.conj(), state2))**2 11 | return fidelity 12 | 13 | if __name__ == "__main__": 14 | state1 = np.array([1, 0]) # |0> state 15 | state2 = np.array([0, 1]) # |1> state 16 | fidelity_calculator = QuantumFidelity() 17 | fidelity = fidelity_calculator.calculate_fidelity(state1, state2) 18 | print("Fidelity:", fidelity) 19 | -------------------------------------------------------------------------------- /src/ai_integration/ai_optimizer.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from scipy.optimize import minimize 3 | 4 | class AIOptimizer: 5 | def __init__(self, objective_function): 6 | self.objective_function = objective_function 7 | 8 | def optimize(self, initial_guess): 9 | """Optimize the given objective function.""" 10 | result = minimize(self.objective_function, initial_guess) 11 | return result 12 | 13 | if __name__ == "__main__": 14 | # Example objective function: f(x) = (x - 3)^2 15 | def objective_function(x): 16 | return (x - 3) ** 2 17 | 18 | optimizer = AIOptimizer(objective_function) 19 | optimal_value = optimizer.optimize(initial_guess=[0]) 20 | print("Optimal Value:", optimal_value) 21 | -------------------------------------------------------------------------------- /src/utils/benchmarking.py: -------------------------------------------------------------------------------- 1 | import time 2 | 3 | class Benchmarking: 4 | @staticmethod 5 | def time_function(func, *args, **kwargs): 6 | """Time the execution of a function.""" 7 | start_time = time.time() 8 | result = func(*args, **kwargs) 9 | end_time = time.time() 10 | execution_time = end_time - start_time 11 | print(f"Function '{func.__name__}' executed in {execution_time:.4f} seconds.") 12 | return result 13 | 14 | if __name__ == "__main__": 15 | def sample_function(n): 16 | """Sample function to demonstrate benchmarking.""" 17 | return sum(range(n)) 18 | 19 | result = Benchmarking.time_function(sample_function, 1000000) 20 | print("Result of sample function:", result) 21 | -------------------------------------------------------------------------------- /src/ai_integration/machine_learning_models/clustering.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from sklearn.cluster import KMeans 3 | 4 | class Clustering: 5 | def __init__(self, n_clusters): 6 | self.model = KMeans(n_clusters=n_clusters) 7 | 8 | def fit(self, data): 9 | """Fit the clustering model to the data.""" 10 | self.model.fit(data) 11 | 12 | def predict(self, data): 13 | """Predict cluster labels for the data.""" 14 | return self.model.predict(data) 15 | 16 | if __name__ == "__main__": 17 | # Example usage 18 | data = np.random.rand(100, 2) # Generate random data 19 | clustering = Clustering(n_clusters=3) 20 | clustering.fit(data) 21 | labels = clustering.predict(data) 22 | print("Cluster Labels:", labels) 23 | -------------------------------------------------------------------------------- /src/security/secure_messaging.py: -------------------------------------------------------------------------------- 1 | class SecureMessaging: 2 | def __init__(self): 3 | self.messages = [] 4 | 5 | def send_message(self, sender, receiver, message): 6 | """Send a secure message.""" 7 | secure_message = f"From: {sender}\nTo: {receiver}\nMessage: {message}" 8 | self.messages.append(secure_message) 9 | print("Message sent securely.") 10 | 11 | def display_messages(self): 12 | """Display all sent messages.""" 13 | print("Sent Messages:") 14 | for msg in self.messages: 15 | print(msg) 16 | 17 | if __name__ == "__main__": 18 | messaging = SecureMessaging() 19 | messaging.send_message("Alice", "Bob", "Hello, Bob! This is a secure message.") 20 | messaging.display_messages() 21 | -------------------------------------------------------------------------------- /tests/integration_tests/test_network.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | from src.network_protocols.network_simulator import NetworkSimulator 3 | import numpy as np 4 | 5 | class TestNetworkSimulator(unittest.TestCase): 6 | def setUp(self): 7 | self.topology = np.array([[0, 1, 0, 1], 8 | [1, 0, 1, 0], 9 | [0, 1, 0, 1], 10 | [1, 0, 1, 0]]) 11 | self.simulator = NetworkSimulator(self.topology) 12 | 13 | def test_network_functionality(self): 14 | performance_metrics = self.simulator.evaluate_performance() 15 | self.assertIn('latency', performance_metrics) # Check if latency is part of metrics 16 | 17 | if __name__ == "__main__": 18 | unittest.main() 19 | -------------------------------------------------------------------------------- /src/ai_integration/machine_learning_models/anomaly_detection.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from sklearn.ensemble import IsolationForest 3 | 4 | class AnomalyDetection: 5 | def __init__(self): 6 | self.model = IsolationForest() 7 | 8 | def fit(self, data): 9 | """Fit the model to the data.""" 10 | self.model.fit(data) 11 | 12 | def predict(self, data): 13 | """Predict anomalies in the data.""" 14 | return self.model.predict(data) 15 | 16 | if __name__ == "__main__": 17 | # Example usage 18 | data = np.random.rand(100, 2) # Generate random data 19 | anomaly_detector = AnomalyDetection() 20 | anomaly_detector.fit(data) 21 | predictions = anomaly_detector.predict(data) 22 | print("Anomaly Predictions:", predictions) 23 | -------------------------------------------------------------------------------- /src/ai_integration/security_ai.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from sklearn.ensemble import IsolationForest 3 | 4 | class SecurityAI: 5 | def __init__(self): 6 | self.model = IsolationForest() 7 | 8 | def train(self, data): 9 | """Train the anomaly detection model.""" 10 | self.model.fit(data) 11 | 12 | def detect_anomalies(self, data): 13 | """Detect anomalies in the given data.""" 14 | predictions = self.model.predict(data) 15 | return predictions 16 | 17 | if __name__ == "__main__": 18 | # Example data: 2D points 19 | data = np.array([[1, 2], [1, 2.5], [1, 3], [10, 10]]) 20 | security_ai = SecurityAI() 21 | security_ai.train(data) 22 | anomalies = security_ai.detect_anomalies(data) 23 | print("Anomaly Detection Results:", anomalies) 24 | -------------------------------------------------------------------------------- /src/ai_integration/machine_learning_models/predictive_model.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from sklearn.linear_model import LinearRegression 3 | 4 | class PredictiveModel: 5 | def __init__(self): 6 | self.model = LinearRegression() 7 | 8 | def train(self, X, y): 9 | """Train the predictive model.""" 10 | self.model.fit(X, y) 11 | 12 | def predict(self, X): 13 | """Make predictions using the trained model.""" 14 | return self.model.predict(X) 15 | 16 | if __name__ == "__main__": 17 | # Example usage 18 | X = np.array([[1], [2], [3], [4]]) 19 | y = np.array([2, 3, 5, 7]) 20 | predictive_model = PredictiveModel() 21 | predictive_model.train(X, y) 22 | predictions = predictive_model.predict(np.array([[5], [6]])) 23 | print("Predictions:", predictions) 24 | -------------------------------------------------------------------------------- /src/simulations/fault_tolerance_simulation.py: -------------------------------------------------------------------------------- 1 | class FaultToleranceSimulation: 2 | def __init__(self): 3 | self.faults = [] 4 | 5 | def introduce_fault(self, fault_type): 6 | """Introduce a fault into the system.""" 7 | self.faults.append(fault_type) 8 | print(f"Fault introduced: {fault_type}") 9 | 10 | def simulate_recovery(self): 11 | """Simulate recovery from faults.""" 12 | if self.faults: 13 | print("Recovering from faults...") 14 | self.faults.clear() 15 | print("Recovery successful.") 16 | else: 17 | print("No faults to recover from.") 18 | 19 | if __name__ == "__main__": 20 | simulation = FaultToleranceSimulation() 21 | simulation.introduce_fault("Quantum Bit Flip") 22 | simulation.simulate_recovery() 23 | -------------------------------------------------------------------------------- /src/network_protocols/error_handling.py: -------------------------------------------------------------------------------- 1 | class ErrorHandling: 2 | def __init__(self): 3 | self.error_log = [] 4 | 5 | def log_error(self, error_message): 6 | """Log an error message.""" 7 | self.error_log.append(error_message) 8 | print(f"Error logged: {error_message}") 9 | 10 | def handle_error(self, error_type): 11 | """Handle different types of errors.""" 12 | if error_type == "quantum_loss": 13 | self.log_error("Quantum state lost during transmission.") 14 | elif error_type == "measurement_error": 15 | self.log_error("Measurement error occurred.") 16 | else: 17 | self.log_error("Unknown error type.") 18 | 19 | if __name__ == "__main__": 20 | error_handler = ErrorHandling() 21 | error_handler.handle_error("quantum_loss") 22 | -------------------------------------------------------------------------------- /examples/basic_example.py: -------------------------------------------------------------------------------- 1 | from src.quantum_comm.entanglement import EntanglementProtocol 2 | from src.quantum_comm.teleportation import QuantumTeleportation 3 | 4 | def main(): 5 | # Basic example of creating entangled states 6 | entanglement_protocol = EntanglementProtocol() 7 | entangled_state = entanglement_protocol.entangled_state() 8 | print("Basic Example: Entangled State:", entangled_state) 9 | 10 | # Basic example of quantum teleportation 11 | teleportation_protocol = QuantumTeleportation() 12 | state_to_teleport = [1, 0] # |0> state 13 | teleportation_circuit = teleportation_protocol.teleport(state_to_teleport) 14 | teleportation_result = teleportation_protocol.simulate(teleportation_circuit) 15 | print("Basic Example: Teleportation Result:", teleportation_result) 16 | 17 | if __name__ == "__main__": 18 | main() 19 | -------------------------------------------------------------------------------- /src/ai_integration/machine_learning_models/reinforcement_learning/environment.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | class Environment: 4 | def __init__(self): 5 | self.state = 0 # Initial state 6 | 7 | def step(self, action): 8 | """Take an action and return the next state and reward.""" 9 | if action == 0: 10 | self.state += 1 # Move to the right 11 | reward = 1 12 | elif action == 1: 13 | self.state -= 1 # Move to the left 14 | reward = -1 15 | else: 16 | reward = 0 # No movement 17 | 18 | self.state = max(0, min(self.state, 4)) # Keep state within bounds 19 | return self.state, reward 20 | 21 | if __name__ == "__main__": 22 | env = Environment() 23 | next_state, reward = env.step(action=0) 24 | print("Next State:", next_state, "Reward:", reward) 25 | -------------------------------------------------------------------------------- /src/security/intrusion_detection.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | class IntrusionDetectionSystem: 4 | def __init__(self): 5 | self.threshold = 0.8 # Threshold for anomaly detection 6 | 7 | def detect_intrusion(self, traffic_data): 8 | """Detect intrusions based on traffic data.""" 9 | # Simulate anomaly detection 10 | anomaly_score = np.random.rand() # Random score for simulation 11 | if anomaly_score > self.threshold: 12 | print("Intrusion detected! Anomaly score:", anomaly_score) 13 | return True 14 | else: 15 | print("No intrusion detected. Anomaly score:", anomaly_score) 16 | return False 17 | 18 | if __name__ == "__main__": 19 | ids = IntrusionDetectionSystem() 20 | traffic_data = np.random.rand(100) # Simulated traffic data 21 | ids.detect_intrusion(traffic_data) 22 | -------------------------------------------------------------------------------- /src/simulations/scalability_tests.py: -------------------------------------------------------------------------------- 1 | import time 2 | 3 | class ScalabilityTests: 4 | def __init__(self): 5 | self.results = [] 6 | 7 | def test_scalability(self, num_nodes): 8 | """Test the scalability of the network with a given number of nodes.""" 9 | start_time = time.time() 10 | # Simulate some processing for scalability 11 | time.sleep(num_nodes * 0.1) # Simulated processing time 12 | end_time = time.time() 13 | execution_time = end time - start_time 14 | self.results.append((num_nodes, execution_time)) 15 | print(f"Scalability test with {num_nodes} nodes took {execution_time:.4f} seconds.") 16 | 17 | if __name__ == "__main__": 18 | scalability_test = ScalabilityTests() 19 | for nodes in range(1, 6): # Testing scalability from 1 to 5 nodes 20 | scalability_test.test_scalability(nodes) 21 | -------------------------------------------------------------------------------- /src/security/threat_modeling.py: -------------------------------------------------------------------------------- 1 | class ThreatModeling: 2 | def __init__(self): 3 | self.threats = [] 4 | 5 | def add_threat(self, threat_name, description): 6 | """Add a threat to the threat model.""" 7 | self.threats.append({'name': threat_name, 'description': description}) 8 | print(f"Threat added: {threat_name}") 9 | 10 | def analyze_threats(self): 11 | """Analyze and display the threats.""" 12 | print("Threat Analysis Report:") 13 | for threat in self.threats: 14 | print(f"- {threat['name']}: {threat['description']}") 15 | 16 | if __name__ == "__main__": 17 | threat_model = ThreatModeling() 18 | threat_model.add_threat("Eavesdropping", "Unauthorized interception of communication.") 19 | threat_model.add_threat("Denial of Service", "Attacks aimed at disrupting service availability.") 20 | threat_model.analyze_threats() 21 | -------------------------------------------------------------------------------- /src/ai_integration/machine_learning_models/neural_networks/transformer_model.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | from tensorflow.keras import layers, models 3 | 4 | class TransformerModel: 5 | def __init__(self, input_shape): 6 | self.model = models.Sequential([ 7 | layers.Input(shape=input_shape), 8 | layers.MultiHeadAttention(num_heads=2, key_dim=2), 9 | layers.GlobalAveragePooling1D(), 10 | layers.Dense(10, activation='softmax') 11 | ]) 12 | 13 | def compile_model(self): 14 | """Compile the Transformer model.""" 15 | self.model.compile (optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) 16 | 17 | if __name__ == "__main__": 18 | input_shape = (None, 64) # Example input shape 19 | transformer = TransformerModel(input_shape) 20 | transformer.compile_model() 21 | print("Transformer Model Compiled.") 22 | -------------------------------------------------------------------------------- /src/ai_integration/natural_language_processing/chatbot.py: -------------------------------------------------------------------------------- 1 | import random 2 | 3 | class Chatbot: 4 | def __init__(self): 5 | self.responses = { 6 | "greeting": ["Hello!", "Hi there!", "Greetings!", "How can I help you?"], 7 | "farewell": ["Goodbye!", "See you later!", "Take care!"] 8 | } 9 | 10 | def respond(self, user_input): 11 | """Generate a response based on user input.""" 12 | if "hello" in user_input.lower(): 13 | return random.choice(self.responses["greeting"]) 14 | elif "bye" in user_input.lower(): 15 | return random.choice(self.responses["farewell"]) 16 | else: 17 | return "I'm not sure how to respond to that." 18 | 19 | if __name__ == "__main__": 20 | chatbot = Chatbot() 21 | user_input = "Hello, chatbot!" 22 | response = chatbot.respond(user_input) 23 | print("Chatbot Response:", response) 24 | -------------------------------------------------------------------------------- /src/ai_integration/cognitive_computing/knowledge_graph.py: -------------------------------------------------------------------------------- 1 | class KnowledgeGraph: 2 | def __init__(self): 3 | self.graph = {} 4 | 5 | def add_relationship(self, subject, predicate, obj): 6 | """Add a relationship to the knowledge graph.""" 7 | if subject not in self.graph: 8 | self.graph[subject] = {} 9 | if predicate not in self.graph[subject]: 10 | self.graph[subject][predicate] = [] 11 | self.graph[subject][predicate].append(obj) 12 | 13 | def query_relationship(self, subject, predicate): 14 | """Query relationships from the knowledge graph.""" 15 | return self.graph.get(subject, {}).get(predicate, []) 16 | 17 | if __name__ == "__main__": 18 | knowledge_graph = KnowledgeGraph() 19 | knowledge_graph.add_relationship("Alice", "knows", "Bob") 20 | relationships = knowledge_graph.query_relationship("Alice", "knows") 21 | print("Relationships:", relationships) 22 | -------------------------------------------------------------------------------- /examples/advanced_example.py: -------------------------------------------------------------------------------- 1 | from src.ai_integration.ai_optimizer import AIOptimizer 2 | from src.network_protocols.routing import RoutingAlgorithm 3 | import numpy as np 4 | 5 | def main(): 6 | # Advanced example of AI optimization 7 | def objective_function(x): 8 | return (x - 3) ** 2 9 | 10 | optimizer = AIOptimizer(objective_function) 11 | optimal_value = optimizer.optimize(initial_guess=[0]) 12 | print("Advanced Example: Optimal Value:", optimal_value) 13 | 14 | # Advanced example of routing algorithm 15 | topology = np.array([[0, 1, 4, 0], 16 | [1, 0, 2, 5], 17 | [4, 2, 0, 1], 18 | [0, 5, 1, 0]]) 19 | routing = RoutingAlgorithm(topology) 20 | shortest_path_distance = routing.dijkstra(0, 3) 21 | print("Advanced Example: Shortest Path Distance from Node 0 to Node 3:", shortest_path_distance) 22 | 23 | if __name__ == "__main__": 24 | main() 25 | -------------------------------------------------------------------------------- /src/network_protocols/network_virtualization.py: -------------------------------------------------------------------------------- 1 | class NetworkVirtualization: 2 | def __init__(self): 3 | self.virtual_networks = {} 4 | 5 | def create_virtual_network(self, network_name): 6 | """Create a virtual network.""" 7 | if network_name not in self.virtual_networks: 8 | self.virtual_networks[network_name] = [] 9 | print(f"Virtual network '{network_name}' created.") 10 | 11 | def add_node_to_virtual_network(self, network_name, node): 12 | """Add a node to a virtual network.""" 13 | if network_name in self.virtual_networks: 14 | self.virtual_networks[network_name].append(node) 15 | print(f"Node '{node}' added to virtual network '{network_name}'.") 16 | 17 | if __name__ == "__main__": 18 | virtualization = NetworkVirtualization() 19 | virtualization.create_virtual_network("Virtual Network 1") 20 | virtualization.add_node_to_virtual_network("Virtual Network 1", "Node A") 21 | -------------------------------------------------------------------------------- /src/quantum_comm/quantum_key_distribution.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from qiskit import QuantumCircuit, Aer, execute 3 | 4 | class QuantumKeyDistribution: 5 | def __init__(self): 6 | self.backend = Aer.get_backend('qasm_simulator') 7 | 8 | def bb84_protocol(self): 9 | """Implement the BB84 quantum key distribution protocol.""" 10 | circuit = QuantumCircuit(2, 2) 11 | circuit.h(0) # Prepare qubit in superposition 12 | circuit.measure(0, 0) # Measure the qubit 13 | return circuit 14 | 15 | def simulate(self, circuit): 16 | """Simulate the quantum circuit and return the result.""" 17 | job = execute(circuit, self.backend) 18 | result = job.result() 19 | counts = result.get_counts() 20 | return counts 21 | 22 | if __name__ == "__main__": 23 | qkd = QuantumKeyDistribution() 24 | circuit = qkd.bb84_protocol() 25 | result = qkd.simulate(circuit) 26 | print("QKD Result:", result) 27 | -------------------------------------------------------------------------------- /src/ai_integration/machine_learning_models/neural_networks/convolutional_network.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | from tensorflow.keras import layers, models 3 | 4 | class ConvolutionalNetwork: 5 | def __init__(self): 6 | self.model = models.Sequential([ 7 | layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)), 8 | layers.MaxPooling2D((2, 2)), 9 | layers.Conv2D(64, (3, 3), activation='relu'), 10 | layers.MaxPooling2D((2, 2)), 11 | layers.Flatten(), 12 | layers.Dense(64, activation='relu'), 13 | layers.Dense(10, activation='softmax') 14 | ]) 15 | 16 | def compile_model(self): 17 | """Compile the CNN model.""" 18 | self.model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) 19 | 20 | if __name__ == "__main__": 21 | cnn = ConvolutionalNetwork() 22 | cnn.compile_model() 23 | print("Convolutional Network Compiled.") 24 | -------------------------------------------------------------------------------- /src/quantum_comm/quantum_measurement.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from qiskit import QuantumCircuit, Aer, execute 3 | 4 | class QuantumMeasurement: 5 | def __init__(self): 6 | self.backend = Aer.get_backend('qasm_simulator') 7 | 8 | def measure_qubit(self): 9 | """Create a circuit to measure a qubit.""" 10 | circuit = QuantumCircuit(1, 1) 11 | circuit.h(0) # Prepare in superposition 12 | ```python 13 | circuit.measure(0, 0) # Measure the qubit 14 | return circuit 15 | 16 | def simulate(self, circuit): 17 | """Simulate the quantum circuit and return the measurement result.""" 18 | job = execute(circuit, self.backend) 19 | result = job.result() 20 | counts = result.get_counts() 21 | return counts 22 | 23 | if __name__ == "__main__": 24 | measurement = QuantumMeasurement() 25 | circuit = measurement.measure_qubit() 26 | result = measurement.simulate(circuit) 27 | print("Measurement Result:", result) 28 | -------------------------------------------------------------------------------- /src/security/cryptographic_protocols.py: -------------------------------------------------------------------------------- 1 | from cryptography.fernet import Fernet 2 | 3 | class CryptographicProtocols: 4 | def __init__(self): 5 | self.key = Fernet.generate_key() 6 | self.cipher = Fernet(self.key) 7 | 8 | def encrypt_message(self, message): 9 | """Encrypt a message using symmetric encryption.""" 10 | encrypted_message = self.cipher.encrypt(message.encode()) 11 | return encrypted_message 12 | 13 | def decrypt_message(self, encrypted_message): 14 | """Decrypt a message using symmetric encryption.""" 15 | decrypted_message = self.cipher.decrypt(encrypted_message).decode() 16 | return decrypted_message 17 | 18 | if __name__ == "__main__": 19 | crypto = CryptographicProtocols() 20 | message = "Hello, secure world!" 21 | encrypted = crypto.encrypt_message(message) 22 | print("Encrypted Message:", encrypted) 23 | decrypted = crypto.decrypt_message(encrypted) 24 | print("Decrypted Message:", decrypted) 25 | -------------------------------------------------------------------------------- /src/network_protocols/cross_layer_optimization.py: -------------------------------------------------------------------------------- 1 | class CrossLayerOptimization: 2 | def __init__(self): 3 | self.optimization_parameters ```python 4 | = {} 5 | 6 | def set_parameter(self, layer, parameter, value): 7 | """Set an optimization parameter for a specific layer.""" 8 | if layer not in self.optimization_parameters: 9 | self.optimization_parameters[layer] = {} 10 | self.optimization_parameters[layer][parameter] = value 11 | print(f"Parameter '{parameter}' for layer '{layer}' set to {value}.") 12 | 13 | def get_parameters(self): 14 | """Get all optimization parameters.""" 15 | return self.optimization_parameters 16 | 17 | if __name__ == "__main__": 18 | optimization = CrossLayerOptimization() 19 | optimization.set_parameter("Network Layer", "Throughput", "1000Mbps") 20 | optimization.set_parameter("Transport Layer", "Latency", "15ms") 21 | print("Current Optimization Parameters:", optimization.get_parameters()) 22 | -------------------------------------------------------------------------------- /src/network_protocols/network_topology.py: -------------------------------------------------------------------------------- 1 | class NetworkTopology: 2 | def __init__(self): 3 | self.topology = {} 4 | 5 | def add_node(self, node): 6 | """Add a node to the network topology.""" 7 | if node not in self.topology: 8 | self.topology[node] = [] 9 | 10 | def add_edge(self, node1, node2): 11 | """Add an edge between two nodes.""" 12 | if node1 in self.topology and node2 in self.topology: 13 | self.topology[node1].append(node2) 14 | self.topology[node2].append(node1) 15 | 16 | def display_topology(self): 17 | """Display the network topology.""" 18 | for node, edges in self.topology.items(): 19 | print(f"{node}: {edges}") 20 | 21 | if __name__ == "__main__": 22 | network_topology = NetworkTopology() 23 | network_topology.add_node("Node A") 24 | network_topology.add_node("Node B") 25 | network_topology.add_edge("Node A", "Node B") 26 | network_topology.display_topology() 27 | -------------------------------------------------------------------------------- /src/simulations/quantum_simulator.py: -------------------------------------------------------------------------------- 1 | from qiskit import QuantumCircuit, Aer, execute 2 | 3 | class QuantumSimulator: 4 | def __init__(self): 5 | self.backend = Aer.get_backend('statevector_simulator') 6 | 7 | def run_circuit(self, circuit): 8 | """Run a quantum circuit and return the state vector.""" 9 | job = execute(circuit, self.backend) 10 | result = job.result() 11 | statevector = result.get_statevector() 12 | return statevector 13 | 14 | def create_sample_circuit(self): 15 | """Create a sample quantum circuit for demonstration.""" 16 | circuit = QuantumCircuit(2) 17 | circuit.h(0) # Apply Hadamard gate to the first qubit 18 | circuit.cx(0, 1) # Apply CNOT gate 19 | return circuit 20 | 21 | if __name__ == "__main__": 22 | simulator = QuantumSimulator() 23 | sample_circuit = simulator.create_sample_circuit() 24 | statevector = simulator.run_circuit(sample_circuit) 25 | print("State Vector:", statevector) 26 | -------------------------------------------------------------------------------- /.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 | -------------------------------------------------------------------------------- /src/quantum_comm/superposition.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from qiskit import QuantumCircuit, Aer, execute 3 | 4 | class SuperpositionProtocol: 5 | def __init__(self): 6 | self.backend = Aer.get_backend('statevector_simulator') 7 | 8 | def create_superposition(self): 9 | """Create a superposition state using a Hadamard gate.""" 10 | circuit = QuantumCircuit(1) 11 | circuit.h(0) # Apply Hadamard gate to create superposition 12 | return circuit 13 | 14 | def simulate(self, circuit): 15 | """Simulate the quantum circuit and return the state vector.""" 16 | job = execute(circuit, self.backend) 17 | result = job.result() 18 | statevector = result.get_statevector() 19 | return statevector 20 | 21 | if __name__ == "__main__": 22 | protocol = SuperpositionProtocol() 23 | superposition_circuit = protocol.create_superposition() 24 | statevector = protocol.simulate(superposition_circuit) 25 | print("Superposition State:", statevector) 26 | -------------------------------------------------------------------------------- /src/network_protocols/adaptive_routing.py: -------------------------------------------------------------------------------- 1 | class AdaptiveRouting: 2 | def __init__(self, topology): 3 | self.topology = topology 4 | 5 | def adaptive_route(self, start, end, traffic_conditions): 6 | """Determine the best route based on current traffic conditions.""" 7 | # Simplified example: choose the route with the least traffic 8 | if traffic_conditions[start][end] < 50: # Example threshold 9 | return f"Route from {start} to {end} is optimal." 10 | else: 11 | return f"Consider alternative routes from {start} to {end}." 12 | 13 | if __name__ == "__main__": 14 | # Example topology and traffic conditions 15 | topology = {} 16 | traffic_conditions = { 17 | "Node A": {"Node B": 30, "Node C": 70}, 18 | "Node B": {"Node A": 30, "Node C": 20}, 19 | "Node C": {"Node A": 70, "Node B": 20} 20 | } 21 | adaptive_routing = AdaptiveRouting(topology) 22 | result = adaptive_routing.adaptive_route("Node A", "Node B", traffic_conditions) 23 | print(result) 24 | -------------------------------------------------------------------------------- /src/quantum_comm/quantum_channel.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from qiskit import QuantumCircuit, Aer, execute 3 | 4 | class QuantumChannel: 5 | def __init__(self): 6 | self.backend = Aer.get_backend('statevector_simulator') 7 | 8 | def apply_noise(self, circuit, noise_type='depolarizing', noise_level=0.1): 9 | """Apply noise to the quantum channel.""" 10 | if noise_type == 'depolarizing': 11 | circuit = circuit.depolarizing_error(noise_level) 12 | return circuit 13 | 14 | def simulate(self, circuit): 15 | """Simulate the quantum circuit and return the state vector.""" 16 | job = execute(circuit, self.backend) 17 | result = job.result() 18 | statevector = result.get_statevector() 19 | return statevector 20 | 21 | if __name__ == "__main__": 22 | channel = QuantumChannel() 23 | circuit = QuantumCircuit(1) 24 | circuit.h(0) # Create a superposition 25 | noisy_circuit = channel.apply_noise(circuit) 26 | statevector = channel.simulate(noisy_circuit) 27 | print("Noisy State:", statevector) 28 | -------------------------------------------------------------------------------- /src/utils/logger.py: -------------------------------------------------------------------------------- 1 | import logging 2 | 3 | class Logger: 4 | def __init__(self, name='UQNLogger', level=logging.INFO): 5 | """Initialize the logger with a specified name and logging level.""" 6 | self.logger = logging.getLogger(name) 7 | self.logger.setLevel(level) 8 | handler = logging.FileHandler('uqn.log') 9 | formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s') 10 | handler.setFormatter(formatter) 11 | self.logger.addHandler(handler) 12 | 13 | def log_info(self, message): 14 | """Log an informational message.""" 15 | self.logger.info(message) 16 | 17 | def log_warning(self, message): 18 | """Log a warning message.""" 19 | self.logger.warning(message) 20 | 21 | def log_error(self, message): 22 | """Log an error message.""" 23 | self.logger.error(message) 24 | 25 | if __name__ == "__main__": 26 | logger = Logger() 27 | logger.log_info("Logger initialized.") 28 | logger.log_warning("This is a warning message.") 29 | logger.log_error("This is an error message.") 30 | -------------------------------------------------------------------------------- /src/quantum_comm/quantum_network_topology.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from qiskit import QuantumCircuit, Aer, execute 3 | 4 | class QuantumNetworkTopology: 5 | def __init__(self): 6 | self.backend = Aer.get_backend('qasm_simulator') 7 | 8 | def create_topology(self, nodes): 9 | """Create a simple quantum network topology.""" 10 | circuit = QuantumCircuit(nodes, nodes) 11 | for i in range(nodes): 12 | circuit.h(i) # Prepare each node in superposition 13 | circuit.measure(range(nodes), range(nodes)) 14 | return circuit 15 | 16 | def simulate(self, circuit): 17 | """Simulate the quantum circuit and return the result.""" 18 | job = execute(circuit, self.backend) 19 | result = job.result() 20 | counts = result.get_counts() 21 | return counts 22 | 23 | if __name__ == "__main__": 24 | nodes = 3 # Number of nodes in the network 25 | topology = QuantumNetworkTopology() 26 | circuit = topology.create_topology(nodes) 27 | result = topology.simulate(circuit) 28 | print("Network Topology Result:", result) 29 | -------------------------------------------------------------------------------- /src/quantum_comm/quantum_state_preparation.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from qiskit import QuantumCircuit, Aer, execute 3 | 4 | class QuantumStatePreparation: 5 | def __init__(self): 6 | self.backend = Aer.get_backend('statevector_simulator') 7 | 8 | def prepare_state(self, state_vector): 9 | """Prepare a quantum state from a given state vector.""" 10 | circuit = QuantumCircuit(len(state_vector)) 11 | circuit.initialize(state_vector, range(len(state_vector))) 12 | return circuit 13 | 14 | def simulate(self, circuit): 15 | """Simulate the quantum circuit and return the state vector.""" 16 | job = execute(circuit, self.backend) 17 | result = job.result() 18 | statevector = result.get_statevector() 19 | return statevector 20 | 21 | if __name__ == "__main__": 22 | state_vector = [1/np.sqrt(2), 1/np.sqrt(2)] # |+> state 23 | state_preparation = QuantumStatePreparation() 24 | circuit = state_preparation.prepare_state(state_vector) 25 | statevector = state_preparation.simulate(circuit) 26 | print("Prepared State:", statevector) 27 | -------------------------------------------------------------------------------- /src/utils/performance_metrics.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | class PerformanceMetrics: 4 | @staticmethod 5 | def calculate_accuracy(true_labels, predicted_labels): 6 | """Calculate accuracy of predictions.""" 7 | return np.mean(np.array(true_labels) == np.array(predicted_labels)) 8 | 9 | @staticmethod 10 | def calculate_precision(true_labels, predicted_labels): 11 | """Calculate precision of predictions.""" 12 | true_positive = np.sum((np.array(predicted_labels) == 1) & (np.array(true_labels) == 1)) 13 | false_positive = np.sum((np.array(predicted_labels) == 1) & (np.array(true_labels) == 0)) 14 | return true_positive / (true_positive + false_positive) if (true_positive + false_positive) > 0 else 0 15 | 16 | if __name__ == "__main__": 17 | true_labels = [1, 0, 1, 1, 0] 18 | predicted_labels = [1, 0, 1, 0, 1] 19 | accuracy = PerformanceMetrics.calculate_accuracy(true_labels, predicted_labels) 20 | precision = PerformanceMetrics.calculate_precision(true_labels, predicted_labels) 21 | print("Accuracy:", accuracy) 22 | print("Precision:", precision) 23 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /src/utils/data_storage.py: -------------------------------------------------------------------------------- 1 | import json 2 | import os 3 | 4 | class DataStorage: 5 | def __init__(self, storage_file='data_storage.json'): 6 | """Initialize the data storage with a specified file.""" 7 | self.storage_file = storage_file 8 | if not os.path.exists(self.storage_file): 9 | with open(self.storage_file, 'w') as file: 10 | json.dump({}, file) # Create an empty JSON file 11 | 12 | def save_data(self, key, value): 13 | """Save data to the storage file.""" 14 | with open(self.storage_file, 'r') as file: 15 | data = json.load(file) 16 | data[key] = value 17 | with open(self.storage_file, 'w') as file: 18 | json.dump(data, file) 19 | 20 | def load_data(self, key): 21 | """Load data from the storage file.""" 22 | with open(self.storage_file, 'r') as file: 23 | data = json.load(file) 24 | return data.get(key) 25 | 26 | if __name__ == "__main__": 27 | storage = DataStorage() 28 | storage.save_data("example_key", "example_value") 29 | value = storage.load_data("example_key") 30 | print("Loaded Value:", value) 31 | -------------------------------------------------------------------------------- /src/ai_integration/machine_learning_models/reinforcement_learning/rl_agent.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | class RLAgent: 4 | def __init__(self, actions): 5 | self.q_table = np.zeros((5, len(actions))) # Example state space of size 5 6 | self.actions = actions 7 | self.learning_rate = 0.1 8 | self.discount_factor = 0.95 9 | 10 | def choose_action(self, state): 11 | """Choose an action based on the current state.""" 12 | return np.argmax(self.q_table[state]) 13 | 14 | def update_q_value(self, state, action, reward, next_state): 15 | """Update the Q-value based on the action taken.""" 16 | best_next_action = np.argmax(self.q_table[next_state]) 17 | td_target = reward + self.discount_factor * self.q_table[next_state][best_next_action] 18 | self.q_table[state][action] += self.learning_rate * (td_target - self.q_table[state][action]) 19 | 20 | if __name__ == "__main__": 21 | actions = [0, 1, 2] # Example actions 22 | agent = RLAgent(actions) 23 | # Example of updating Q-values 24 | agent.update_q_value(state=0, action=1, reward=10, next_state=1) 25 | print("Q-Table:", agent.q_table) 26 | -------------------------------------------------------------------------------- /src/network_protocols/multicast_protocol.py: -------------------------------------------------------------------------------- 1 | class MulticastProtocol: 2 | def __init__(self): 3 | self.groups = {} 4 | 5 | def create_group(self, group_name): 6 | """Create a multicast group.""" 7 | if group_name not in self.groups: 8 | self.groups[group_name] = [] 9 | print(f"Group '{group_name}' created.") 10 | 11 | def join_group(self, group_name, member): 12 | """Add a member to a multicast group.""" 13 | if group_name in self.groups: 14 | self.groups[group_name].append(member) 15 | print(f"{member} joined group '{group_name}'.") 16 | 17 | def send_message(self, group_name, message): 18 | """Send a message to all members of a multicast group.""" 19 | if group_name in self.groups: 20 | for member in self.groups[group_name]: 21 | print(f"Sending message to {member}: {message}") 22 | 23 | if __name__ == "__main__": 24 | multicast = MulticastProtocol() 25 | multicast.create_group("Group 1") 26 | multicast.join_group("Group 1", "Alice") 27 | multicast.join_group("Group 1", "Bob") 28 | multicast.send_message("Group 1", "Hello, Group 1!") 29 | -------------------------------------------------------------------------------- /src/quantum_comm/quantum_cryptography.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from qiskit import QuantumCircuit, Aer, execute 3 | 4 | class QuantumCryptography: 5 | def __init__(self): 6 | self.backend = Aer.get_backend('qasm_simulator') 7 | 8 | def create_encrypted_message(self, message): 9 | """Encrypt a message using quantum techniques.""" 10 | circuit = QuantumCircuit(len(message), len(message)) 11 | for i, bit in enumerate(message): 12 | if bit == '1': 13 | circuit.x(i) # Apply X gate for '1' 14 | circuit.measure(range(len(message)), range(len(message))) 15 | return circuit 16 | 17 | def simulate(self, circuit): 18 | """Simulate the quantum circuit and return the result.""" 19 | job = execute(circuit, self.backend) 20 | result = job.result() 21 | counts = result.get_counts() 22 | return counts 23 | 24 | if __name__ == "__main__": 25 | message = '101' # Example binary message 26 | cryptography = QuantumCryptography() 27 | circuit = cryptography.create_encrypted_message(message) 28 | result = cryptography.simulate(circuit) 29 | print("Encrypted Message Result:", result) 30 | -------------------------------------------------------------------------------- /src/quantum_comm/entanglement.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from qiskit import QuantumCircuit, Aer, execute 3 | 4 | class EntanglementProtocol: 5 | def __init__(self): 6 | self.backend = Aer.get_backend('statevector_simulator') 7 | 8 | def create_entangled_pair(self): 9 | """Create an entangled pair of qubits using a Bell state.""" 10 | circuit = QuantumCircuit(2) 11 | circuit.h(0) # Apply Hadamard gate to the first qubit 12 | circuit.cx(0, 1) # Apply CNOT gate to create entanglement 13 | return circuit 14 | 15 | def simulate(self, circuit): 16 | """Simulate the quantum circuit and return the state vector.""" 17 | job = execute(circuit, self.backend) 18 | result = job.result() 19 | statevector = result.get_statevector() 20 | return statevector 21 | 22 | def entangled_state(self): 23 | """Generate and return the entangled state.""" 24 | circuit = self.create_entangled_pair() 25 | statevector = self.simulate(circuit) 26 | return statevector 27 | 28 | if __name__ == "__main__": 29 | protocol = EntanglementProtocol() 30 | entangled_state = protocol.entangled_state() 31 | print("Entangled State:", entangled_state) 32 | -------------------------------------------------------------------------------- /src/simulations/network_simulator.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | class NetworkSimulator: 4 | def __init__(self, topology): 5 | self.topology = topology # Adjacency matrix representing the network 6 | 7 | def simulate_traffic(self, source, destination, traffic_load): 8 | """Simulate network traffic from source to destination.""" 9 | # Simplified simulation: assume traffic is evenly distributed 10 | path_length = self.find_path_length(source, destination) 11 | if path_length > 0: 12 | return traffic_load / path_length 13 | else: 14 | return 0 # No path found 15 | 16 | def find_path_length(self, source, destination): 17 | """Find the length of the path between source and destination.""" 18 | # For simplicity, return a fixed path length 19 | return np.random.randint(1, 5) # Random path length for simulation 20 | 21 | if __name__ == "__main__": 22 | topology = np.array([[0, 1, 0, 1], 23 | [1, 0, 1, 0], 24 | [0, 1, 0, 1], 25 | [1, 0, 1, 0]]) 26 | simulator = NetworkSimulator(topology) 27 | traffic = simulator.simulate_traffic(0, 3, traffic_load=100) 28 | print("Simulated Traffic Load:", traffic) 29 | -------------------------------------------------------------------------------- /src/quantum_comm/quantum_error_correction.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from qiskit import QuantumCircuit, Aer, execute 3 | 4 | class QuantumErrorCorrection: 5 | def __init__(self): 6 | self.backend = Aer.get_backend('qasm_simulator') 7 | 8 | def encode(self, state): 9 | """Encode a qubit into a three-qubit code.""" 10 | circuit = QuantumCircuit(3) 11 | circuit.initialize(state, 0) 12 | circuit.cx(0, 1) 13 | circuit.cx(0, 2) 14 | return circuit 15 | 16 | def decode(self, circuit): 17 | """Decode the three-qubit code back to a single qubit.""" 18 | circuit.cx(1, 0) 19 | circuit.cx(2, 0) 20 | return circuit 21 | 22 | def simulate(self, circuit): 23 | """Simulate the quantum circuit and return the result.""" 24 | job = execute(circuit, self.backend) 25 | result = job.result() 26 | counts = result.get_counts() 27 | return counts 28 | 29 | if __name__ == "__main__": 30 | state_to_encode = [1, 0] # |0> state 31 | error_correction = QuantumErrorCorrection() 32 | circuit = error_correction.encode(state_to_encode) 33 | circuit = error_correction.decode(circuit) 34 | result = error_correction.simulate(circuit) 35 | print("Error Correction Result:", result) 36 | -------------------------------------------------------------------------------- /src/network_protocols/congestion_control.py: -------------------------------------------------------------------------------- 1 | class CongestionControl: 2 | def __init__(self): 3 | self.congestion_level = 0 # 0: No congestion, 1: Low, 2: Medium, 3: High 4 | 5 | def detect_congestion(self, traffic): 6 | """Detect congestion based on traffic levels.""" 7 | if traffic > 80: 8 | self.congestion_level = 3 # High congestion 9 | elif traffic > 50: 10 | self.congestion_level = 2 # Medium congestion 11 | elif traffic > 20: 12 | self.congestion_level = 1 # Low congestion 13 | else: 14 | self.congestion_level = 0 # No congestion 15 | 16 | def take_action(self): 17 | """Take action based on congestion level.""" 18 | if self.congestion_level == 3: 19 | print("High congestion detected! Reducing data transmission rate.") 20 | elif self.congestion_level == 2: 21 | print("Medium congestion detected! Monitoring traffic.") 22 | elif self.congestion_level == 1: 23 | print("Low congestion detected. Normal operation.") 24 | else: 25 | print("No congestion detected. All systems normal.") 26 | 27 | if __name__ == "__main__": 28 | congestion_control = CongestionControl() 29 | congestion_control.detect_congestion(85) # Example traffic level 30 | congestion_control.take_action() 31 | -------------------------------------------------------------------------------- /src/quantum_comm/teleportation.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from qiskit import QuantumCircuit, Aer, execute 3 | 4 | class QuantumTeleportation: 5 | def __init__(self): 6 | self.backend = Aer.get_backend('statevector_simulator') 7 | 8 | def teleport(self, state): 9 | """Teleport a quantum state using a Bell state.""" 10 | circuit = QuantumCircuit(3, 3) 11 | 12 | # Prepare the state to be teleported 13 | circuit.initialize(state, 0) 14 | 15 | # Create entangled pair 16 | circuit.h(1) 17 | circuit.cx(1, 2) 18 | 19 | # Bell measurement 20 | circuit.cx(0, 1) 21 | circuit.h(0 circuit.measure([0, 1], [0, 1]) 22 | 23 | # Apply corrections based on measurement 24 | circuit.cx(1, 2) 25 | circuit.cz(0, 2) 26 | 27 | # Measure the teleported state 28 | circuit.measure(2, 2) 29 | 30 | return circuit 31 | 32 | def simulate(self, circuit): 33 | """Simulate the quantum circuit and return the result.""" 34 | job = execute(circuit, self.backend) 35 | result = job.result() 36 | counts = result.get_counts() 37 | return counts 38 | 39 | if __name__ == "__main__": 40 | state_to_teleport = [1, 0] # |0> state 41 | teleportation = QuantumTeleportation() 42 | circuit = teleportation.teleport(state_to_teleport) 43 | result = teleportation.simulate(circuit) 44 | print("Teleportation Result:", result) 45 | -------------------------------------------------------------------------------- /src/quantum_comm/quantum_teleportation_network.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from qiskit import QuantumCircuit, Aer, execute 3 | 4 | class QuantumTeleportationNetwork: 5 | def __init__(self): 6 | self.backend = Aer.get_backend('statevector_simulator') 7 | 8 | def teleportation_protocol(self, state): 9 | """Implement a networked teleportation protocol.""" 10 | circuit = QuantumCircuit(3, 3) 11 | 12 | # Prepare the state to be teleported 13 | circuit.initialize(state, 0) 14 | 15 | # Create entangled pair 16 | circuit.h(1) 17 | circuit.cx(1, 2) 18 | 19 | # Bell measurement 20 | circuit.cx(0, 1) 21 | circuit.h(0) 22 | circuit.measure([0, 1], [0, 1]) 23 | 24 | # Apply corrections based on measurement 25 | circuit.cx(1, 2) 26 | circuit.cz(0, 2) 27 | 28 | # Measure the teleported state 29 | circuit.measure(2, 2) 30 | 31 | return circuit 32 | 33 | def simulate(self, circuit): 34 | """Simulate the quantum circuit and return the result.""" 35 | job = execute(circuit, self.backend) 36 | result = job.result() 37 | counts = result.get_counts() 38 | return counts 39 | 40 | if __name__ == "__main__": 41 | state_to_teleport = [1, 0] # |0> state 42 | teleportation_network = QuantumTeleportationNetwork() 43 | circuit = teleportation_network.teleportation_protocol(state_to_teleport) 44 | result = teleportation_network.simulate(circuit) 45 | print("Networked Teleportation Result:", result) 46 | -------------------------------------------------------------------------------- /docs/tutorials/advanced_usage.md: -------------------------------------------------------------------------------- 1 | # Advanced Usage of the Universal Quantum Network (UQN) 2 | 3 | ## Overview 4 | 5 | This document provides advanced users and developers with insights into utilizing the full potential of the Universal Quantum Network (UQN). It covers complex scenarios, optimization techniques, and best practices. 6 | 7 | ## Advanced Scenarios 8 | 9 | ### 1. Multi-Node Communication 10 | 11 | - **Setup**: Learn how to configure multiple quantum nodes to communicate simultaneously. 12 | - **Example**: Implement a multi-node entanglement protocol to enhance data transfer rates. 13 | 14 | ### 2. Error Correction Techniques 15 | 16 | - **Overview**: Understand the importance of quantum error correction in maintaining data integrity. 17 | - **Implementation**: Use the Shor Code to protect your quantum information during transmission. 18 | 19 | ### 3. Integrating AI for Optimization 20 | 21 | - **Predictive Modeling**: Implement AI algorithms to predict network congestion and optimize routing. 22 | - **Anomaly Detection**: Set up machine learning models to monitor and respond to unusual network behavior. 23 | 24 | ## Best Practices 25 | 26 | - **Code Modularity**: Keep your code modular to enhance readability and maintainability. 27 | - **Documentation**: Document your code and experiments thoroughly to facilitate collaboration and future development. 28 | - **Testing**: Regularly test your code against the latest updates in the UQN framework to ensure compatibility. 29 | 30 | ## Conclusion 31 | 32 | By following the advanced usage guidelines, you can maximize the effectiveness of the Universal Quantum Network and contribute to its ongoing development and improvement. 33 | -------------------------------------------------------------------------------- /src/network_protocols/routing.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | class RoutingAlgorithm: 4 | def __init__(self, topology): 5 | self.topology = topology # Adjacency matrix representing the network 6 | 7 | def dijkstra(self, start, end): 8 | """Implement Dijkstra's algorithm for shortest path routing.""" 9 | num_nodes = len(self.topology) 10 | visited = [False] * num_nodes 11 | distances = [float('inf')] * num_nodes 12 | distances[start] = 0 13 | 14 | for _ in range(num_nodes): 15 | min_distance = float('inf') 16 | min_index = -1 17 | for v in range(num_nodes): 18 | if not visited[v] and distances[v] < min_distance: 19 | min_distance = distances[v] 20 | min_index = v 21 | 22 | visited[min_index] = True 23 | 24 | for v in range(num_nodes): 25 | if (self.topology[min_index][v] > 0 and 26 | not visited[v] and 27 | distances[min_index] + self.topology[min_index][v] < distances[v]): 28 | distances[v] = distances[min_index] + self.topology[min_index][v] 29 | 30 | return distances[end] 31 | 32 | if __name__ == "__main__": 33 | # Example topology (adjacency matrix) 34 | topology = np.array([[0, 1, 4, 0], 35 | [1, 0, 2, 5], 36 | [4, 2, 0, 1], 37 | [0, 5, 1, 0]]) 38 | routing = RoutingAlgorithm(topology) 39 | shortest_path_distance = routing.dijkstra(0, 3) 40 | print("Shortest Path Distance from Node 0 to Node 3:", shortest_path_distance) 41 | -------------------------------------------------------------------------------- /docs/tutorials/getting_started.md: -------------------------------------------------------------------------------- 1 | # Getting Started with the Universal Quantum Network (UQN) 2 | 3 | ## Overview 4 | 5 | This guide is designed to help new users and developers get started with the Universal Quantum Network (UQN). It covers the installation process, basic concepts, and how to run your first quantum communication experiment. 6 | 7 | ## Prerequisites 8 | 9 | Before you begin, ensure you have the following: 10 | 11 | - A basic understanding of quantum mechanics. 12 | - Python 3.6 or higher installed on your machine. 13 | - Access to a quantum computing framework (e.g., Qiskit or Cirq). 14 | 15 | ## Installation 16 | 17 | 1. **Clone the Repository**: 18 | 19 | ```bash 20 | 1 git clone https://github.com/KOSASIH/UniversalQuantumNetwork.git 21 | 2 cd UniversalQuantumNetwork 22 | ``` 23 | 24 | 2. **Install Dependencies**: Use pip to install the required packages. 25 | 26 | ```bash 27 | 1 pip install -r requirements.txt 28 | ``` 29 | 30 | 3. **Set Up the Environment**: Configure your environment variables as needed for your quantum computing framework. 31 | 32 | ## Running Your First Experiment 33 | 34 | 1. **Create a New Experiment**: Navigate to the experiments/ directory and create a new Python file for your experiment. 35 | 36 | 2. **Write Your Code**: Use the provided templates to set up your quantum circuit and define your communication protocol. 37 | 38 | 3. **Execute the Experiment**: Run your experiment using the following command: 39 | 40 | ```bash 41 | 1 python your_experiment_file.py 42 | ``` 43 | 44 | 4. **Analyze Results**: Review the output and logs generated by your experiment to understand the results. 45 | 46 | ## Conclusion 47 | You are now ready to explore the capabilities of the Universal Quantum Network! For more advanced usage and examples, refer to the advanced_usage.md tutorial. 48 | -------------------------------------------------------------------------------- /docs/research_papers/relevant_papers.md: -------------------------------------------------------------------------------- 1 | # Relevant Research Papers and Articles 2 | 3 | ## Overview 4 | 5 | This document lists key research papers and articles that provide insights into the principles and advancements related to the Universal Quantum Network (UQN). These resources are valuable for understanding the theoretical foundations and practical applications of quantum communication. 6 | 7 | ## Key Papers 8 | 9 | 1. **"Quantum Key Distribution: A Review"** 10 | - Authors: Bennett, C. H., Brassard, G. 11 | - Year: 1984 12 | - Summary: This foundational paper introduces the concept of quantum key distribution (QKD) and presents the BB84 protocol. 13 | 14 | 2. **"Entanglement-Based Quantum Communication"** 15 | - Authors: Bouwmeester, D., Pan, J.-W., Mattle, K., Zeilinger, A. 16 | - Year: 1997 17 | - Summary: This paper discusses the principles of entanglement and its application in quantum communication. 18 | 19 | 3. **"Quantum Teleportation"** 20 | - Authors: Bouwmeester, D., Pan, J.-W., Mattle, K., Zeilinger, A. 21 | - Year: 1997 22 | - Summary: The authors describe the experimental realization of quantum teleportation, a key concept in quantum communication. 23 | 24 | 4. **"Quantum Error Correction"** 25 | - Authors: Shor, P. W. 26 | - Year: 1995 27 | - Summary: This paper introduces the concept of quantum error correction and presents the Shor code, which is essential for maintaining the integrity of quantum information. 28 | 29 | 5. **"A Survey of Quantum Communication Protocols"** 30 | - Authors: Scarani, V., Bechmann-Pasquinucci, H., Briegel, H.-J., et al. 31 | - Year: 2009 32 | - Summary: This survey provides an overview of various quantum communication protocols, including QKD and entanglement-based communication. 33 | 34 | ## Conclusion 35 | 36 | These research papers and articles are crucial for anyone looking to deepen their understanding of quantum communication and its applications within the Universal Quantum Network. They provide a solid foundation for further exploration and innovation in the field. 37 | -------------------------------------------------------------------------------- /docs/protocols.md: -------------------------------------------------------------------------------- 1 | # Communication Protocols in the Universal Quantum Network (UQN) 2 | 3 | ## Overview 4 | 5 | The Universal Quantum Network (UQN) employs a variety of communication protocols to facilitate secure and efficient data transmission between quantum nodes. These protocols leverage the unique properties of quantum mechanics to achieve their objectives. 6 | 7 | ## Key Protocols 8 | 9 | ### 1. Quantum Key Distribution (QKD) 10 | 11 | - **Description**: QKD is a method for secure key exchange that uses quantum mechanics to ensure that any eavesdropping attempts can be detected. 12 | - **Implementation**: The UQN implements protocols such as BB84 and E91 for QKD. 13 | 14 | ### 2. Entanglement-Based Communication 15 | 16 | - **Description**: This protocol utilizes entangled particles to enable instantaneous communication between nodes. 17 | - **Mechanism**: Information is encoded in the quantum states of entangled particles, allowing for real-time data transfer. 18 | 19 | ### 3. Quantum Teleportation 20 | 21 | - **Description**: Quantum teleportation allows for the transfer of quantum states from one node to another without physically transmitting the particle itself. 22 | - **Process**: Involves entangling two nodes and using classical communication to complete the teleportation process. 23 | 24 | ### 4. Error Correction Protocols 25 | 26 | - **Description**: Quantum error correction protocols are essential for maintaining the integrity of quantum information during transmission. 27 | - **Examples**: The UQN employs protocols such as the Shor Code and the Steane Code. 28 | 29 | ### 5. Classical Control Protocols 30 | 31 | - **Description**: Classical protocols are used for managing the control signals and routing information in the network. 32 | - **Examples**: TCP/IP protocols adapted for quantum communication. 33 | 34 | ## Conclusion 35 | 36 | The communication protocols in the UQN are designed to ensure secure, efficient, and reliable data transmission, leveraging the principles of quantum mechanics to overcome the limitations of classical communication systems. 37 | -------------------------------------------------------------------------------- /docs/architecture.md: -------------------------------------------------------------------------------- 1 | # Architecture of the Universal Quantum Network (UQN) 2 | 3 | ## Overview 4 | 5 | The architecture of the Universal Quantum Network (UQN) is designed to facilitate secure, instantaneous communication across vast distances using quantum mechanics principles. The architecture consists of several key components that work together to achieve the project's goals. 6 | 7 | ## Key Components 8 | 9 | ### 1. Quantum Nodes 10 | 11 | - **Description**: Quantum nodes are the fundamental building blocks of the UQN. Each node is capable of performing quantum operations, including entanglement, superposition, and measurement. 12 | - **Functionality**: Nodes can communicate with each other using quantum channels, enabling instantaneous data transfer. 13 | 14 | ### 2. Quantum Channels 15 | 16 | - **Description**: Quantum channels are the pathways through which quantum information is transmitted between nodes. 17 | - **Types**: 18 | - **Entangled Channels**: Used for instantaneous communication. 19 | - **Classical Channels**: Used for classical data transmission and control signals. 20 | 21 | ### 3. Control Layer 22 | 23 | - **Description**: The control layer manages the operations of the quantum nodes and channels. 24 | - **Functionality**: It handles routing, error correction, and synchronization of quantum operations. 25 | 26 | ### 4. AI Integration 27 | 28 | - **Description**: AI algorithms are integrated into the UQN to optimize network performance and enhance security. 29 | - **Functionality**: AI is used for anomaly detection, predictive modeling, and adaptive routing. 30 | 31 | ### 5. Security Framework 32 | 33 | - **Description**: The security framework ensures the integrity and confidentiality of the data transmitted over the network. 34 | - **Components**: 35 | - **Quantum Key Distribution (QKD)**: Provides secure key exchange. 36 | - **Intrusion Detection System (IDS)**: Monitors for unauthorized access and anomalies. 37 | 38 | ## Conclusion 39 | 40 | The architecture of the UQN is designed to be scalable, secure, and efficient, enabling advanced quantum communication capabilities for both terrestrial and interstellar applications. 41 | -------------------------------------------------------------------------------- /docs/case_studies/real_world_applications.md: -------------------------------------------------------------------------------- 1 | # Real-World Applications of the Universal Quantum Network (UQN) 2 | 3 | ## Overview 4 | 5 | The Universal Quantum Network (UQN) has the potential to revolutionize various industries by enabling secure and instantaneous communication. This document highlights several real-world applications of the UQN. 6 | 7 | ## Key Applications 8 | 9 | ### 1. Secure Financial Transactions 10 | 11 | - **Description**: The UQN can facilitate secure financial transactions by utilizing quantum key distribution (QKD) to protect sensitive data. 12 | - **Example**: Banks can implement QKD to secure online transactions, ensuring that customer data remains confidential. 13 | 14 | ### 2. Quantum-Enhanced Telemedicine 15 | 16 | - **Description**: The UQN can improve telemedicine by enabling secure communication between healthcare providers and patients. 17 | - **Example**: Doctors can share sensitive medical data securely using quantum communication, enhancing patient privacy. 18 | 19 | ### 3. Government and Military Communications 20 | 21 | - **Description**: The UQN can provide secure communication channels for government and military operations, protecting sensitive information from interception. 22 | - **Example**: Military organizations can use quantum communication to coordinate operations without the risk of eavesdropping. 23 | 24 | ### 4. Secure Cloud Computing 25 | 26 | - **Description**: The UQN can enhance the security of cloud computing services by implementing quantum encryption methods. 27 | - **Example**: Companies can store sensitive data in the cloud while ensuring that it is protected by quantum encryption techniques. 28 | 29 | ### 5. Research and Development 30 | 31 | - **Description**: The UQN can facilitate collaboration among researchers by providing secure communication channels for sharing sensitive research data. 32 | - **Example**: Academic institutions can collaborate on sensitive projects without the risk of data breaches. 33 | 34 | ## Conclusion 35 | 36 | The real-world applications of the Universal Quantum Network demonstrate its potential to transform industries by providing secure, instantaneous communication solutions. As the technology continues to develop, the UQN will play a crucial role in shaping the future of communication. 37 | -------------------------------------------------------------------------------- /docs/technologies.md: -------------------------------------------------------------------------------- 1 | # Technologies and Frameworks Utilized in the Universal Quantum Network (UQN) 2 | 3 | ## Overview 4 | 5 | The Universal Quantum Network (UQN) leverages a variety of advanced technologies and frameworks to achieve its goals of secure and instantaneous quantum communication. This document outlines the key technologies used in the project. 6 | 7 | ## Key Technologies 8 | 9 | ### 1. Quantum Computing Frameworks 10 | 11 | - **Qiskit**: An open-source quantum computing framework developed by IBM, used for building quantum circuits and algorithms. 12 | - **Cirq**: A Python library for quantum computing developed by Google, designed for creating, editing, and invoking quantum circuits. 13 | 14 | ### 2. Quantum Communication Technologies 15 | 16 | - **Quantum Repeaters**: Devices that extend the range of quantum communication by overcoming the limitations of distance and loss in quantum channels. 17 | - **Quantum Sensors**: Technologies that utilize quantum mechanics to achieve high precision measurements, enhancing the capabilities of the UQN. 18 | 19 | ### 3. Classical Computing Infrastructure 20 | 21 | - **Cloud Computing**: Utilized for processing and storing classical data, providing the necessary computational resources for the UQN. 22 | - **Microservices Architecture**: A design approach that allows for the development of modular services, facilitating scalability and maintainability of the UQN system. 23 | 24 | ### 4. Security Technologies 25 | 26 | - **Blockchain**: Implemented for secure transaction logging and to enhance the integrity of data exchanged within the network. 27 | - **Intrusion Detection Systems (IDS)**: Employed to monitor network traffic for suspicious activities and potential security breaches. 28 | 29 | ### 5. AI and Machine Learning 30 | 31 | - **Predictive Analytics**: AI algorithms are used to analyze network performance and predict potential issues before they arise. 32 | - **Anomaly Detection**: Machine learning models are implemented to identify unusual patterns in data transmission, enhancing security measures. 33 | 34 | ## Conclusion 35 | 36 | The technologies and frameworks utilized in the UQN are integral to its functionality, enabling secure, efficient, and advanced quantum communication capabilities that push the boundaries of current communication systems. 37 | -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # Contributing to Universal Quantum Network (UQN) 2 | 3 | Thank you for your interest in contributing to the Universal Quantum Network (UQN) project! We welcome contributions from the community and appreciate your efforts to help improve the project. Please follow the guidelines below to ensure a smooth contribution process. 4 | 5 | ## Table of Contents 6 | 7 | - [Getting Started](#getting-started) 8 | - [Reporting Issues](#reporting-issues) 9 | - [Code Contributions](#code-contributions) 10 | - [Documentation Contributions](#documentation-contributions) 11 | - [Coding Standards](#coding-standards) 12 | - [Commit Messages](#commit-messages) 13 | - [Pull Request Process](#pull-request-process) 14 | - [Code of Conduct](#code-of-conduct) 15 | 16 | ## Getting Started 17 | 18 | 1. **Fork the Repository**: Click the "Fork" button on the top right of the repository page to create your own copy of the project. 19 | 2. **Clone Your Fork**: Clone your forked repository to your local machine. 20 | 21 | ```bash 22 | 1 git clone https://github.com/KOSASIH/UniversalQuantumNetwork.git 23 | 2 cd UniversalQuantumNetwork 24 | ``` 25 | 26 | 3. **Create a Branch**: Create a new branch for your feature or bug fix. 27 | 28 | ```bash 29 | 1 git checkout -b feature/your-feature-name 30 | ``` 31 | 32 | ## Reporting Issues 33 | If you encounter any bugs or have feature requests, please report them using the following steps: 34 | 35 | 1. **Check Existing Issues**: Before creating a new issue, check the issues section to see if your issue has already been reported. 36 | 2. **Create a New Issue**: If your issue is not listed, click on the "New Issue" button and provide the following information: 37 | - A clear and descriptive title. 38 | - A detailed description of the issue, including steps to reproduce it. 39 | - Any relevant screenshots or error messages. 40 | 41 | 3. Code Contributions 42 | To contribute code to the UQN project, please follow these steps: 43 | 44 | 1. **Make Your Changes**: Implement your changes in your local branch. 45 | 2. **Test Your Changes**: Ensure that your changes do not break existing functionality. Run the tests provided in the tests/ directory. 46 | 3. **Add New Tests**: If you are adding new features, consider adding tests to cover your changes. 47 | 48 | ## Documentation Contributions 49 | We appreciate contributions to the documentation! If you notice any errors or have suggestions for improvement, please follow the same process as for code contributions: 50 | 51 | 1. **Edit Documentation**: Make your changes in the appropriate documentation files located in the docs/ directory. 52 | 2. **Preview Changes**: If possible, preview your changes to ensure they render correctly. 53 | 54 | ## Coding Standards 55 | To maintain code quality and consistency, please adhere to the following coding standards: 56 | 57 | - Follow PEP 8 for Python code style. 58 | - Use meaningful variable and function names. 59 | - Write clear and concise comments to explain complex logic. 60 | - Organize code into functions and classes where appropriate. 61 | 62 | ## Commit Messages 63 | When committing your changes, please use clear and descriptive commit messages. Follow this format: 64 | 65 | ``` 66 | 1 [Type] Short description of the change 67 | 2 68 | 3 Detailed explanation of the change, if necessary. Include any relevant issue numbers. 69 | ``` 70 | 71 | Types: 72 | 73 | - Fix: A bug fix. 74 | - Feature: A new feature or enhancement. 75 | - Docs: Documentation changes. 76 | - Test: Adding or updating tests. 77 | - Chore: Other changes that do not modify src or test files. 78 | 79 | ## Pull Request Process 80 | 81 | 1. **Push Your Changes**: Push your changes to your forked repository. 82 | 83 | ```bash 84 | 1 git push origin feature/your-feature-name 85 | ``` 86 | 87 | 2. **Open a Pull Request**: Go to the original repository and click on the "Pull Requests" tab. Click "New Pull Request" and select your branch. 88 | 3. **Provide a Description**: Fill out the pull request template, providing a clear description of your changes and any relevant context. 89 | 4. **Review Process**: Your pull request will be reviewed by the project maintainers. Be open to feedback and make any necessary changes. 90 | 91 | ## Code of Conduct 92 | By participating in this project, you agree to abide by our Code of Conduct. We are committed to providing a welcoming and inclusive environment for all contributors. 93 | 94 | Thank you for contributing to the Universal Quantum Network project! Your efforts help us build a better and more advanced quantum communication framework. 95 | -------------------------------------------------------------------------------- /docs/code_structure.md: -------------------------------------------------------------------------------- 1 | UniversalQuantumNetwork/ 2 | │ 3 | ├── README.md # Overview of the project 4 | ├── CONTRIBUTING.md # Guidelines for contributing to the project 5 | ├── LICENSE # License information for the project 6 | │ 7 | ├── docs/ # Documentation folder 8 | │ ├── architecture.md # Detailed architecture of the UQN 9 | │ ├── protocols.md # Communication protocols used in the UQN 10 | │ ├── technologies.md # Technologies and frameworks utilized 11 | │ ├── tutorials/ # Tutorials for users and developers 12 | │ │ ├── getting_started.md # Getting started guide 13 | │ │ └── advanced_usage.md # Advanced usage scenarios 14 | │ ├── research_papers/ # Links to relevant research papers and articles 15 | │ └── case_studies/ # Real-world applications and case studies 16 | │ 17 | ├── src/ # Source code folder 18 | │ ├── quantum_comm/ # Quantum communication algorithms 19 | │ │ ├── entanglement.py # Implementation of entanglement protocols 20 | │ │ ├── superposition.py # Algorithms for superposition-based communication 21 | │ │ ├── quantum_channel.py # Quantum channel management 22 | │ │ ├── teleportation.py # Quantum teleportation algorithms 23 | │ │ ├── quantum_error_correction.py # Error correction protocols 24 | │ │ ├── quantum_key_distribution.py # QKD implementation 25 | │ │ ├── quantum_state_preparation.py # State preparation techniques 26 | │ │ ├── quantum_measurement.py # Measurement protocols 27 | │ │ ├── quantum_cryptography.py # Advanced cryptographic protocols 28 | │ │ ├── quantum_network_topology.py # Topology management for quantum networks 29 | │ │ ├── quantum_fidelity.py # Fidelity calculations for quantum states 30 | │ │ └── quantum_teleportation_network.py # Networked teleportation protocols 31 | │ │ 32 | │ ├── ai_integration/ # AI integration components 33 | │ │ ├── ai_optimizer.py # AI optimization algorithms 34 | │ │ ├── security_ai.py # AI for security enhancements 35 | │ │ ├── machine_learning_models/ # ML models for data analysis 36 | │ │ │ ├── anomaly_detection.py # Anomaly detection algorithms 37 | │ │ │ ├── predictive_model.py # Predictive modeling for network performance 38 | │ │ │ ├── clustering.py # Clustering algorithms for data analysis 39 | │ │ │ ├── reinforcement_learning/ # RL algorithms for adaptive network management 40 | │ │ │ │ ├── rl_agent.py # Reinforcement learning agent 41 | │ │ │ │ └── environment.py # Environment setup for RL training 42 | │ │ │ └── neural_networks/ # Advanced neural network architectures 43 | │ │ │ ├── convolutional_network.py # CNN for image data analysis 44 | │ │ │ ├── recurrent_network.py # RNN for time-series data 45 | │ │ │ └── transformer_model.py # Transformer for NLP tasks 46 | │ │ ├── natural_language_processing/ # NLP for user interaction 47 | │ │ │ ├── nlp_engine.py # NLP engine for command processing 48 | │ │ │ └── chatbot.py # Chatbot for user support 49 | │ │ └── cognitive_computing/ # Cognitive computing components 50 | │ │ ├── reasoning_engine.py # Reasoning and inference engine 51 | │ │ └── knowledge_graph.py # Knowledge graph management 52 | │ │ 53 | │ ├── network_protocols/ # Network protocols 54 | │ │ ├── routing.py # Routing algorithms for quantum networks 55 | │ │ ├── error_handling.py # Error handling mechanisms 56 | │ │ ├── congestion_control.py # Congestion control algorithms 57 | │ │ ├── network_topology.py # Network topology management 58 | │ │ ├── multicast_protocol.py # Multicast communication protocols 59 | │ │ ├── quality_of_service.py # QoS management protocols 60 | │ │ ├── adaptive_routing.py # Adaptive routing algorithms for dynamic networks 61 | │ │ ├── network_virtualization.py # Virtualization techniques for quantum networks 62 | │ │ └── cross_layer_optimization.py # Cross-layer optimization strategies 63 | │ │ 64 | │ ├── utils/ # Utility functions and helpers 65 | │ │ ├── logger.py # Logging utilities 66 | │ │ ├── config.py # Configuration management 67 | │ │ ├── data_storage.py # Data storage and retrieval utilities 68 | │ │ ├── visualization.py # Visualization tools for data analysis 69 | │ │ ├── performance_metrics.py # Performance metrics calculation 70 | │ │ └── benchmarking.py # Benchmarking tools for performance evaluation 71 | │ │ 72 | │ ├── simulations/ # Simulation tools for testing and validation 73 | │ │ ├── quantum_simulator.py # Quantum circuit simulator 74 | │ │ ├── network_simulator.py # Network performance simulator 75 | │ │ ├── scenario_generator.py # Scenario generation for testing 76 | │ │ ├── fault_tolerance_simulation.py # Simulations for fault tolerance 77 | │ │ └── scalability_tests.py # Tests for scalability of the network 78 | │ │ 79 | │ └── security/ # Security components 80 | │ ├── intrusion_detection.py # Intrusion detection system 81 | │ ├── threat_modeling.py # Threat modeling and analysis 82 | │ ├── security_audit.py # Security audit tools 83 | │ ├── cryptographic_protocols.py # Advanced cryptographic protocols 84 | │ └── secure_messaging.py # Secure messaging protocols for communication 85 | │ 86 | ├── examples/ # Example implementations 87 | │ ├── basic_example.py # Basic usage example of UQN 88 | │ ├── advanced_example.py # Advanced usage example 89 | │ ├── real_time_data_processing.py # Example for real-time data processing 90 | │ └── demo/ # Demo scripts for showcasing features 91 | │ ├── demo_entanglement.py # Demo for entanglement communication 92 | │ ├── demo_ai_integration.py # Demo for AI integration 93 | │ ├── demo_security.py # Demo for security features 94 | │ └── demo_network_performance.py # Demo for network performance evaluation 95 | │ 96 | ├── tests/ # Testing folder 97 | │ ├── unit_tests/ # Unit tests for individual components 98 | │ │ ├── test_entanglement.py # Tests for entanglement algorithms 99 | │ │ ├── test_routing.py # Tests for routing protocols 100 | │ │ ├── test_ai_optimizer.py # Tests for AI optimization 101 | │ │ ├── test_error_handling.py # Tests for error handling 102 | │ │ ├── test_intrusion_detection.py # Tests for intrusion detection 103 | │ │ ├── test_quality_of_service.py # Tests for QoS management 104 | │ │ └── test_neural_networks.py # Tests for neural network components 105 | │ │ 106 | │ └── integration_tests/ # Integration tests for the entire system 107 | │ ├── test_network.py # Tests for network functionality 108 | │ ├── test_quantum_comm.py # Tests for quantum communication 109 | │ ├── test_security.py # Tests for security features 110 | │ └── test_ai_integration.py # Tests for AI integration 111 | │ 112 | ├── scripts/ # Scripts for deployment and management 113 | │ ├── deploy.sh # Deployment script for the UQN 114 | │ ├── setup_env.sh # Environment setup script 115 | │ ├── run_simulations.sh # Script to run simulations for testing 116 | │ ├── backup_data.sh # Script for backing up data 117 | │ └── update_dependencies.sh # Script to update project dependencies 118 | │ 119 | └── images/ # Images and diagrams for documentation 120 | ├── architecture_diagram.png # Architecture diagram of UQN 121 | ├── flowchart.png # Flowchart of communication processes 122 | ├── ai_integration_diagram.png # Diagram illustrating AI integration in UQN 123 | ├── security_model_diagram.png # Diagram of the security model in UQN 124 | └── network_topology_diagram.png # Diagram of network topology 125 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | [![NASA Certified Space Explorer](https://img.shields.io/badge/NASA%20Certified%20Space%20Explorer-NASA-blue.svg)](https://www.nasa.gov/) 2 | [![ESA Certified Space 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Universal Quantum Network ( UQN ) by KOSASIH is licensed under Creative Commons Attribution 4.0 International

71 | 72 | # UniversalQuantumNetwork 73 | The Universal Quantum Network (UQN) project aims to develop a groundbreaking global and interstellar communication framework that harnesses the principles of quantum entanglement, superposition, and advanced artificial intelligence. This repository serves as a collaborative platform for researchers, developers, and enthusiasts to contribute to the design and implementation of a seamless, ultra-secure, and instantaneous communication system. The UQN seeks to transcend the limitations of classical networks, enabling real-time data exchange across vast distances, including interplanetary and interstellar scales. 74 | 75 | Join us in exploring the future of communication and computation! 76 | 77 | # Universal Quantum Network (UQN) 78 | 79 | ## Overview 80 | 81 | The **Universal Quantum Network (UQN)** is an ambitious project aimed at creating a global and interstellar communication and computation framework that leverages the principles of quantum mechanics, including entanglement and superposition. By integrating advanced artificial intelligence, the UQN seeks to establish a seamless, ultra-secure, and instantaneous communication system that transcends the limitations of classical networks. This project enables real-time data exchange across vast distances, including interplanetary and interstellar scales. 82 | 83 | ## Goals 84 | 85 | - **Ultra-Secure Communication**: Utilize quantum key distribution and advanced cryptographic protocols to ensure secure data transmission. 86 | - **Instantaneous Data Exchange**: Leverage quantum entanglement to facilitate instantaneous communication across vast distances. 87 | - **Scalability**: Design a network that can scale to accommodate future advancements in quantum technology and increasing communication demands. 88 | - **AI Integration**: Employ artificial intelligence to optimize network performance, enhance security, and facilitate user interaction. 89 | - **Interstellar Communication**: Develop protocols and technologies that enable communication with potential extraterrestrial civilizations. 90 | 91 | ## Features 92 | 93 | - **Quantum Communication Protocols**: Implementations of quantum entanglement, teleportation, and superposition-based communication. 94 | - **AI-Driven Optimization**: Advanced AI algorithms for network optimization, anomaly detection, and predictive modeling. 95 | - **Robust Security Framework**: Intrusion detection, threat modeling, and secure messaging protocols. 96 | - **Simulation Tools**: Quantum circuit and network performance simulators for testing and validation. 97 | - **Extensive Documentation**: Comprehensive documentation, tutorials, and case studies to support users and developers. 98 | 99 | ## Installation 100 | 101 | To get started with the UQN project, follow these steps: 102 | 103 | 1. **Clone the Repository**: 104 | ```bash 105 | 1 git clone https://github.com/KOSASIH/UniversalQuantumNetwork.git 106 | 2 cd UniversalQuantumNetwork 107 | ``` 108 | 109 | 2. **Set Up the Environment**: It is recommended to use a virtual environment. You can create one using venv or conda. 110 | 111 | Using 'venv': 112 | 113 | ```bash 114 | 1 python -m venv venv 115 | 2 source venv/bin/activate # On Windows use `venv\Scripts\activate` 116 | ``` 117 | 118 | 3. **Install Dependencies**: Install the required packages using pip: 119 | 120 | ```bash 121 | 1 pip install -r requirements.txt 122 | ``` 123 | 124 | 4. **Run Setup Script (if applicable)**: If there is a setup script, run it to configure the environment: 125 | 126 | ```bash 127 | 1 bash setup_env.sh 128 | ``` 129 | 130 | ## Usage 131 | 132 | ### Basic Example 133 | To run a basic example of the UQN, execute the following command: 134 | 135 | ```bash 136 | 1 python examples/basic_example.py 137 | ``` 138 | 139 | ### Advanced Usage 140 | For advanced usage scenarios, refer to the tutorials in the docs/tutorials/ directory. You can find guides on: 141 | 142 | - Setting up a quantum communication link 143 | - Integrating AI for network optimization 144 | - Implementing secure messaging protocols 145 | 146 | ## Contributing 147 | We welcome contributions from the community! To contribute to the UQN project, please follow these guidelines: 148 | 149 | 1. Fork the Repository: Create a personal fork of the repository on GitHub. 150 | 2. Create a Branch: Create a new branch for your feature or bug fix. 151 | 152 | ```bash 153 | 1 git checkout -b feature/your-feature-name 154 | ``` 155 | 156 | 4. Make Changes: Implement your changes and commit them with descriptive messages. 157 | 5. Push Changes: Push your changes to your forked repository. 158 | 159 | ```bash 160 | 1 git push origin feature/your-feature-name 161 | ``` 162 | 163 | 7. Create a Pull Request: Open a pull request to the main repository, describing your changes and their purpose. 164 | 165 | For more detailed guidelines, please refer to the [CONTRIBUTING.md](CONTRIBUTING.md) file. 166 | 167 | ## License 168 | This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details. 169 | 170 | ## Acknowledgments 171 | Special thanks to the contributors and researchers in the field of quantum computing and communication. 172 | This project builds upon the foundational work of various quantum computing frameworks and libraries. 173 | 174 | --------------------------------------------------------------------------------