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
- Memory Tracker — monitors quantum memory availability across all nodes
- Priority Scheduler — schedules objectives offline based on priority
- Resource Allocator — assigns resources globally (not per-layer)
- 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:
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
priority = f(urgency, fidelity_requirement, qubit_count, deadline)
sorted_tasks = sort(tasks, key=priority, reverse=True)
Step 3: Scheduling
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