Quantum Biomimetic Distributed Intelligence (QBDI) Implementation

This repository contains a Python implementation of the Quantum Biomimetic Distributed Intelligence (QBDI) framework for UAV swarm coordination as described in the research paper.

Overview

QBDI is a novel framework that integrates quantum computing principles with biologically inspired intelligence systems for UAV swarm coordination. The implementation includes:

1. QAOA-based Quantum Decision Model - Optimizes UAV decision states using quantum-inspired algorithms
2. Entropy-Based Swarm Coordination - Measures and optimizes swarm coherence
3. Quantum Stigmergic Communication (SQEC) - Enables implicit coordination through an entanglement-inspired model
4. Mycelial Memory Networks (MMN) - Bio-inspired memory structure for distributed information storage

Repository Structure

• src/ - Source code for all QBDI components
  • qaoa_decision_model.py - Implementation of the QAOA-based quantum decision model
  • entropy_swarm_coordination.py - Implementation of entropy-based swarm coordination
  • quantum_stigmergic_communication.py - Implementation of quantum stigmergic communication
  • mycelial_memory_network.py - Implementation of mycelial memory networks
  • uav_swarm_simulation.py - Integration of all components into a UAV swarm simulation
• results/ - Visualization outputs and simulation results
• test_qbdi.py - Test script for all components

Installation

# Clone the repository
git clone https://github.com/RoshanRaghavander/QDBI-Algorithm.git
cd QDBI-Algorithm

# Install required packages
pip install numpy matplotlib scipy networkx scikit-learn pandas seaborn tqdm

Usage

Running the Simulation

from src.uav_swarm_simulation import QBDISwarmSimulation

# Create and run simulation
simulation = QBDISwarmSimulation(num_uavs=10, world_size=100.0, obstacle_count=5)
results = simulation.run_simulation(num_steps=100)

# Visualize results
simulation.visualize_simulation(results)
simulation.create_animation(results)

# Print metrics
print(f"Success Rate: {results['success_rate']:.1f}%")
print(f"Collision Rate: {results['collision_rate']:.1f}%")
print(f"Average Energy Usage: {results['energy_usage']:.1f} J/UAV")
print(f"Decision Latency: {results['decision_latency']:.1f} ms")

Testing Individual Components

# Test QAOA Decision Model
from src.qaoa_decision_model import QAOADecisionModel
model = QAOADecisionModel(5)
# ... (see test_qbdi.py for examples)

# Test Entropy-Based Swarm Coordination
from src.entropy_swarm_coordination import EntropySwarmCoordination
# ... (see test_qbdi.py for examples)

# Test Quantum Stigmergic Communication
from src.quantum_stigmergic_communication import QuantumStigmergicCommunication
# ... (see test_qbdi.py for examples)

# Test Mycelial Memory Networks
from src.mycelial_memory_network import MycelialMemoryNetwork
# ... (see test_qbdi.py for examples)

Mathematical Formulation

QAOA-Based Quantum Decision Model

The Hamiltonian function governing UAV decisions:

HQBDI = ∑(i,j) Jij zi zj + ∑i hi zi

Where:

• zi ∈ {-1, 1} represents the decision state of UAV i
• Jij represents interaction strength between UAVs
• hi represents an external field (environmental influence)

Entropy-Based Swarm Coordination

Shannon entropy function for measuring swarm coherence:

Hswarm(t) = -∑i Pi log Pi

Where Pi represents the probability of UAV i following an optimal trajectory.

Quantum Stigmergic Communication

Implicit coordination model:

Sij(t) = ∫E Γ(pi, pj) · χ(ei, ej)de

Where:

• Γ(pi, pj) = exp(-‖pi - pj‖²/2σ²) is the spatial correlation function
• χ(ei, ej) measures entanglement strength between UAV states

Results

See the results/ directory for visualizations and the results_summary.md file for a detailed analysis of the simulation results.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Based on the research paper "Quantum Biomimetic Distributed Intelligence (QBDI): A Quantum-Inspired Framework for UAV Swarm Coordination" by Roshan Raghavander N.