Quantum Network Simulation Framework

A framework for simulating quantum network protocols including Q-CAST and Q-PASS.

Installation

pip install numpy networkx matplotlib

Quick Start

Custom Configuration

config = SimulationConfig(
    num_nodes=100,        # Number of nodes in network
    target_Ep=0.6,        # Target entanglement probability
    q=0.9,               # Quantum memory quality
    link_state_range=3,   # Link state exchange range
    average_degree=6,     # Average node degree
    num_requests=10,      # Number of S-D pairs
    num_slots=50         # Time slots for simulation
)
results = run_simulations(SIMULATION_TYPES, config)

Available Simulators

1. Q-CAST (EXT)

   • Uses Extended Dijkstra's Algorithm
   • Implements XOR-based recovery
   • Optimized for quantum memory efficiency

2. Q-CAST Enhanced

   • Advanced path selection
   • Adaptive recovery strategies
   • Historical path optimization

3. Q-PASS Variants

   • CR (Channel Reliability)
   • SumDist (Sum of distances)
   • BotCap (Bottleneck Capacity)

Adding a New Simulator

To add your own simulator class to the framework:

1. Create Your Simulator Class

from quantum_network import QuantumNetwork

class MyCustomSimulator(QuantumNetwork):
    def simulate(self) -> Dict[str, List[float]]:
        """Implement your simulation logic here"""
        # Your implementation
        return {
            'throughput': throughput_list,
            'success_rate': success_rates,
            'path_reliability': reliabilities
        }

2. Add to config.py

# In config.py
from simulators import MyCustomSimulator

# Add to SIMULATION_TYPES list
SIMULATION_TYPES = [
    # ... existing simulators ...
    SimulationType(
        name="MY_CUSTOM",
        simulator_class=MyCustomSimulator,
        routing_metric="YOUR_METRIC",  # e.g., "EXT", "CR", "SumDist"
        display_name="My Custom Simulator"
    )
]

3. Required Methods Your simulator class must implement:

• simulate(): Main simulation method
• attempt_entanglement(path): Entanglement attempt logic
• attempt_entanglement_with_recovery(path, s, d): Recovery mechanism

4. Return Format The simulate() method should return a dictionary with metrics:

{
    'throughput': List[float],      # Entanglements per slot
    'success_rate': List[float],    # Success rates
    'path_reliability': List[float] # Path reliabilities
}

Project Structure

├── main.py                  # Main entry point
├── quantum_network.py       # Core network implementation
├── simulators.py           # Simulation implementations
├── topology.py             # Topology management
├── config.py               # Configuration settings
├── path_selection.py       # Path selection algorithms
├── recovery_strategies.py  # Recovery implementations
├── ext_plotting.py        # External plotting utilities