Quantum Network Control

---
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

```python
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

1. Define network topology and link parameters
2. Compute expected fidelity for each path
3. Select optimal swapping strategy per route
4. Simulate or analyze throughput/latency tradeoffs
5. 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