| name | quantum-network-control |
| description | Optimize entanglement distribution in quantum networks via link-layer control strategies. Compare sequential vs simultaneous entanglement swapping for multi-hop quantum communication. Use when: (1) designing quantum network architectures, (2) optimizing entanglement distribution, (3) comparing quantum repeater strategies, (4) quantum internet protocol design, (5) entanglement swapping optimization, (6) quantum network link-layer control.
|
Quantum Network Control
Optimize entanglement distribution and swapping in multi-hop quantum networks.
Based on sequential vs simultaneous entanglement swapping analysis (arXiv:2605.04047).
Core Concepts
Entanglement Swapping Strategies
Sequential Swapping:
- Nodes act on local state information only
- Each hop waits for previous hop to complete
- Simpler but higher latency
- Lower resource requirements per node
Simultaneous Swapping:
- Multiple nodes perform Bell measurements concurrently
- Requires global coordination or pre-agreed schedules
- Lower latency but higher resource demands
- Better throughput for long-distance links
Progressive Swapping to the Middle (PSM): (Updated 2026-06-02, arXiv:2605.31493)
- Establish entanglement in parallel on all segments, then progressively swap toward the middle
- Minimizes memory holding time: O(log n) vs O(n) for sequential
- Specifically adapted for networks with imperfect quantum memories
- When to use: Multi-hop networks where memory decoherence is the bottleneck (T_coh < (n-1) × t_swap)
- Key advantage: Memory-aware scheduling accounts for heterogeneous node coherence times
- See
progressive-swapping-quantum-network-protocol skill for detailed PSM implementation patterns
Key Metrics
- Fidelity: Quality of end-to-end entangled state
- Rate: Entangled pairs generated per second
- Latency: Time from request to entanglement delivery
- Memory requirements: Qubits needed at each node
Optimization Framework
Step 1: Model the Network
network = {
"nodes": ["A", "B", "C", "D"],
"links": [
("A", "B", {"distance_km": 50, "fidelity": 0.95}),
("B", "C", {"distance_km": 50, "fidelity": 0.95}),
("C", "D", {"distance_km": 50, "fidelity": 0.95}),
],
"memory_coherence_time_ms": 100,
"bell_measurement_fidelity": 0.98,
}
Step 2: Choose Swapping Strategy
| Criterion | Sequential | Simultaneous |
|---|
| Latency | O(n) rounds | O(1) rounds |
| Memory per node | 1 qubit | O(degree) qubits |
| Coordination | Local | Global |
| Best for | Long chains | Dense networks |
Step 3: Optimize Link-Layer Control
- Scheduling: When to attempt entanglement generation
- Buffering: How long to store entangled pairs
- Routing: Which path to use for multi-hop connections
- Purification: When to apply entanglement distillation
Workflow
- Define network topology and link parameters
- Compute expected fidelity for each path
- Select optimal swapping strategy per route
- Simulate or analyze throughput/latency tradeoffs
- Tune memory management and purification thresholds
Key References
- Entanglement swapping optimization: arXiv:2605.04047
- Progressive Swapping to the Middle (PSM) protocol: arXiv:2605.31493 - see
progressive-swapping-quantum-network-protocol skill for memory-aware PSM implementation
- Related: quantum-systems-engineering, distributed-quantum-computing, progressive-swapping-quantum-network-protocol
Limitations
- Assumes perfect classical communication for coordination
- Fidelity models may not capture all hardware imperfections
- Scaling to large networks requires approximation methods
