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:

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