2. System Architecture

Quantum Layer

• Simulates quantum principles for data exploration and decision parallelization.

• Enables multi-state evaluation with probabilistic models.

Code Example: Quantum Layer Simulation

import numpy as np

class QuantumLayer:
    def simulate_superposition(self, data):
        states = np.random.rand(len(data))
        return np.multiply(data, states)

# Example Usage
data = [1, 2, 3, 4]
quantum_layer = QuantumLayer()
superposed_states = quantum_layer.simulate_superposition(data)
print("Quantum States:", superposed_states)

Agent Layer

• Network of autonomous agents collaborating dynamically.

• Implements self-learning and task-specific expertise.

Code Example: Agent Class

class Agent:
    def __init__(self, name, specialization):
        self.name = name
        self.specialization = specialization

    def execute_task(self, task):
        return f"{self.name} ({self.specialization}) executed task: {task}"

# Example Usage
agent = Agent("Agent_1", "Logistics Optimization")
print(agent.execute_task("Optimize Delivery Routes"))

Hybrid Integration Layer

• Combines quantum and classical outputs into actionable insights.

• Ensures interpretability and coherence.

Code Example: Hybrid Integration

class HybridIntegration:
    def integrate(self, classical_data, quantum_data):
        combined = [(c + q) / 2 for c, q in zip(classical_data, quantum_data)]
        return combined

# Example Usage
classical_data = [10, 20, 30]
quantum_data = [12, 18, 28]
integration = HybridIntegration()
result = integration.integrate(classical_data, quantum_data)
print("Integrated Data:", result)

Reasoning Stage

• Synthesizes outputs from all agents.

• Uses feedback loops to refine results iteratively.

Code Example: Feedback Loop

class ReasoningStage:
    def refine_results(self, initial_results, feedback):
        refined = [res * (1 + fb) for res, fb in zip(initial_results, feedback)]
        return refined

# Example Usage
results = [0.8, 0.6, 0.9]
feedback = [0.1, -0.05, 0.2]
reasoning_stage = ReasoningStage()
refined = reasoning_stage.refine_results(results, feedback)
print("Refined Results:", refined)

Security and Privacy

• Quantum-resistant cryptography secures communications.

• Federated learning ensures data privacy.

Code Example: Secure Communication

from cryptography.fernet import Fernet

class SecureCommunication:
    def __init__(self):
        self.key = Fernet.generate_key()
        self.cipher = Fernet(self.key)

    def encrypt_message(self, message):
        return self.cipher.encrypt(message.encode())

    def decrypt_message(self, encrypted_message):
        return self.cipher.decrypt(encrypted_message).decode()

# Example Usage
secure_comm = SecureCommunication()
message = "Confidential Data"
encrypted = secure_comm.encrypt_message(message)
print("Encrypted:", encrypted)
decrypted = secure_comm.decrypt_message(encrypted)
print("Decrypted:", decrypted)