Quantum Network Task Control

---
name: quantum-network-task-control
description: >
  Centralized task-based quantum network control framework. Resource-centric approach
  replacing layered protocol stacks. Centralized controller tracks quantum memory
  availability across nodes and schedules objectives via priority-based scheduler.
  Use when: quantum network architecture, centralized quantum control, task-based
  quantum networking, quantum memory scheduling, SeQUeNCe simulator, quantum network
  scaling, arXiv:2605.03336.
---

# Centralized Task-based Quantum Network Control

Resource-centric, task-based quantum network control using centralized controller
instead of traditional layered protocol stacks.

## Problem with Layered Stacks

Traditional layered quantum network architectures:
- Impose stringent design and timing constraints
- Add latency to entanglement generation requests
- State degradation from increasing delays
- Minimize achievable fidelities

## Centralized Controller Architecture

### Core Components

1. **Memory Tracker** — monitors quantum memory availability across all nodes
2. **Priority Scheduler** — schedules objectives offline based on priority
3. **Resource Allocator** — assigns resources globally (not per-layer)
4. **Task Queue** — manages entanglement generation requests

### Workflow

```
Request → Queue → Priority Sort → Memory Check → Schedule → Execute → Deliver
```

## Key Findings from Simulation (SeQUeNCe)

### Topology Performance

| Topology | Request Delivery | Delay Profile |
|----------|-----------------|---------------|
| Caveman | High fraction delivered | Some high-delay requests |
| Grid | High fraction delivered | Some high-delay requests |
| Star | Lower fraction delivered | CDFs saturate quickly |
| Bottleneck | Moderate | Variable |

### Scalability Results

- **Linear CDF shift** with queue size across all topologies
- **Star topology**: priority queue CDFs converge to saturation at high request rates
- **Caveman/Grid**: robust for high-load scenarios

## Implementation Pattern

### Step 1: Resource Discovery

Map all quantum memories, their states, and connectivity:
```python
resource_map = {
    node_id: {
        'memories': [mem_state for mem in node.memories],
        'links': [(neighbor, fidelity) for neighbor in node.links],
        'capacity': node.total_memory_slots
    }
}
```

### Step 2: Task Prioritization

```python
priority = f(urgency, fidelity_requirement, qubit_count, deadline)
sorted_tasks = sort(tasks, key=priority, reverse=True)
```

### Step 3: Scheduling

```python
for task in sorted_tasks:
    if resources_available(task, resource_map):
        schedule(task, resource_map)
    else:
        queue(task)  # retry on next cycle
```

## Comparison: Layered vs Centralized

| Aspect | Layered | Centralized |
|--------|---------|-------------|
| Latency | High (per-layer processing) | Low (direct scheduling) |
| State Fidelity | Degraded by delays | Preserved |
| Scalability | Limited by stack depth | Linear scaling |
| Flexibility | Rigid interfaces | Adaptive allocation |
| Overhead | Per-layer state | Single controller state |

## Applicable Protocols

- Entanglement distribution
- Quantum key distribution (QKD)
- Teleportation routing
- Quantum repeater management

## Activation Keywords

- quantum network centralized control
- task-based quantum networking
- quantum memory scheduling
- SeQUeNCe quantum simulator
- quantum network topology
- priority quantum scheduler
- resource-centric quantum network

## Related Skills

- **quantum-network-control**: Entanglement distribution optimization
- **distributed-quantum-computing**: Distributed quantum architecture
- **quantum-data-centers-entanglement**: Quantum data center networks