Quantum Network Entanglement Simulation Engine
Simulate entanglement distribution across repeater networks, model link generation protocols, fidelity purification, routing strategies, QKD key distribution, quantum teleportation, and distributed quantum computing.
Simulate entanglement generation and purification across quantum repeater networks with configurable topology, routing strategies, and stochastic link models.
Learn more →Run thousands of simulation trials to measure success rates, mean latency, fidelity distributions, and per-link utilization across your network.
Learn more →Run BB84-style quantum key distribution protocols between nodes. Inspect QBER, secret key length, and protocol efficiency.
Learn more →Teleport quantum states across multi-hop paths with optional relay nodes. Compare relayed vs direct performance.
Learn more →Coordinate multi-party quantum computations using star, ring, mesh, or arbitrary topologies with GHZ / cluster / graph measurement bases.
Learn more →Author, validate, diff, and version-control network topologies as JSON. Build configs programmatically or reload from disk.
Learn more →Three steps to your first simulation: install, define a network, and request entanglement.
pip install qnet-corefrom qnet_core import (
QNetEngine, NodeDefinition, LinkDefinition,
StrategyType,
)
engine = QNetEngine()
engine.define_network(
nodes=[
NodeDefinition(id="Alice", memory_lifetime_t2=1.0),
NodeDefinition(id="Bob", memory_lifetime_t2=1.0),
],
links=[
LinkDefinition("Alice", "Bob", distance_km=10.0,
base_fidelity=0.95, generation_rate_hz=1000.0),
],
)
result = engine.request_entanglement(
from_node="Alice",
to="Bob",
fidelity_target=0.90,
max_latency_ms=5000.0,
strategy=StrategyType.HighestFidelity,
)
print(f"Success: {result.success}")
print(f"Fidelity: {result.final_fidelity:.4f}")
print(f"Path: {' → '.join(result.execution_path)}")