PaperlayerView exact revision on arXiv

Source-linked AI summary

QUASAR: Quantum Satellite Architecture and Routing Simulator

Yaliang Shi, Zi Wang, Bangguo Yuan, Gaojie Wu, Zhiwei Zhao

arXiv:2609.04920v1cs.NI

TL;DR

Satellite quantum-network protocols are difficult to evaluate under dynamic orbital geometry, changing optical channels, and quantum-memory constraints. QUASAR addresses this with a lightweight decoupled simulator that models these effects as network-layer attributes, supports representative architectures and routing workloads, and reduces update latency by over 85% versus continuous polling.

  • Problem

    Evaluating satellite quantum-network protocols under spatiotemporal orbital dynamics and quantum physical constraints is computationally expensive.

  • Method

    QUASAR decouples continuous physical calculations from discrete-event simulation, exposing orbital, channel, and memory states as network-layer attributes while modeling two satellite architectures and an EDR-aware routing workload.

  • Results

    Over 85% lower network-layer update latency than continuous polling was achieved, with the gain attributed to combined visibility, channel, and fidelity event filtering.

  • Takeaways & Limitations

    QUASAR provides a practical and extensible framework for evaluating satellite quantum-network architectures, routing workloads, orbital traces, concurrent requests, and event-driven execution.

  • Takeaways & Limitations

    The concurrent-request experiment is not a standalone algorithmic benchmark or a full resource-contention scheduling model.

Abstract

from arXiv · show

The deployment of Low Earth Orbit (LEO) satellite constellations is an important step toward global-scale quantum networking. However, evaluating satellite quantum network protocols under spatiotemporal orbital dynamics and quantum physical constraints remains computationally expensive and challenging. In this paper, we propose QUASAR, a lightweight simulator for evaluating entanglement distribution in satellite-based quantum networks. QUASAR provides a decoupled architecture that integrates dynamic orbital topologies, time-varying optical transmittance, and quantum memory decoherence into network- layer attributes. To demonstrate its capabilities, we abstract and implement two representative hardware architectures: Simultaneous Downlink and On-Orbit Stitching. We further introduce an Entanglement Distribution Rate (EDR)-Aware Spatiotemporal Routing (EASR) heuristic as a reference workload. Our case study examines how QUASAR supports different satellite architectures, routing workloads, realistic orbital traces, concurrent requests, and scalable event-driven execution. With over 85% lower network-layer update latency than continuous polling, QUASAR provides a practical and extensible framework for future satellite quantum network protocol evaluation.

1 Introduction

QUASAR addresses the difficulty of evaluating satellite quantum networks under changing orbital, optical, and memory conditions. It provides a lightweight, decoupled simulation framework and evaluates representative architectures and routing workloads.

  • LEO constellations motivate systematic tools for global-scale entanglement distribution because terrestrial optical-fiber photon loss grows exponentially.
  • Existing simulators trade detailed quantum-physical modeling for scalability, while routing studies often evaluate isolated or averaged physical conditions.
  • QUASAR exposes orbital geometry, optical transmittance, and memory decoherence as dynamic network-layer attributes for event-driven routing evaluation.
  • The framework formalizes Simultaneous Downlink and On-Orbit Stitching, including temporal decoherence models for quantum memories.
  • The case study uses EDR-Aware Spatiotemporal Routing as a reference workload across architecture behavior, orbital traces, concurrent requests, and runtime ablation.

2 Background and Related Work

Satellite quantum networking relies on end-to-end entanglement rather than classical packet forwarding, but its protocols must handle intermittent links and time-varying physical constraints. QUASAR provides a lightweight abstraction layer connecting these constraints with programmable network-layer evaluation.

  • Quantum networks distribute high-fidelity EPR pairs as consumable resources for teleportation and distributed quantum applications.
  • Quantum teleportation consumes a shared EPR pair, while entanglement swapping combines local EPR pairs into a longer end-to-end pair.
  • Satellite links are spatiotemporal because attenuation varies with distance and geometry, contacts are intermittent, and stored qubits undergo fidelity degradation.
  • Prior work spans detailed quantum-network simulators, satellite channel models, and routing or resource-allocation studies with different abstraction goals.
  • QUASAR complements these tools by integrating orbital visibility, optical transmittance, and memory-related fidelity degradation into dynamic network-layer attributes.

3 The QUASAR Simulator Design

QUASAR bridges continuous satellite-physics calculations and discrete-event network simulation by exposing orbital visibility, channel transmittance, and memory decoherence as evolving network-layer attributes. Its lightweight physical models capture spatial attenuation, relay success, and storage-induced fidelity degradation for routing evaluation.

  • Spatiotemporal abstraction: QUASAR abstracts orbital visibility, channel transmittance, and memory decoherence into evolving attributes consumed by discrete-event simulation.The overlay bridges continuous physical calculations and discrete-event network simulation.
  • Spatiotemporal abstraction: At each 100 ms simulation slot, the topology engine evaluates orbital visibility, channel transmittance, and memory state for configurable LEO constellations.It supports Walker-Delta constellations, TLE sets, and SGP4-based trajectory propagation.
  • Dynamic channel modeling: The engine prunes space-to-ground edges below the hardware-dictated elevation threshold, preventing routing over blocked or low-elevation connections.Visibility is enforced through a Line-of-Sight mask based on instantaneous geometry.
  • Dynamic channel modeling: QUASAR differentiates satellite-ground and inter-satellite transmittance, incorporating free-space propagation loss and additional atmospheric penalties for satellite-ground links.The atmospheric penalty increases as elevation decreases toward the horizon.
  • Quantum-physical abstractions: The physical model tracks multi-hop swapping success and storage-induced fidelity degradation, exposing both as dynamic attributes for higher-layer routing.Relay success is modeled across intermediate swapping satellites, while memory fidelity depends on storage duration and coherence time.

4 Satellite Architectures and Routing

QUASAR represents satellite hardware and routing as distinct spatiotemporal decision spaces. It supports memoryless simultaneous downlinks and memory-assisted stitching, while EASR filters infeasible paths by fidelity and ranks feasible paths using combined optical and temporal costs.

  • Satellite architectures: QUASAR supports two operational paradigms whose routing decision spaces differ according to onboard quantum-memory capability.The interface maintains a dynamic topology and exposes physical attributes for network-layer evaluation.
  • Satellite architectures: Simultaneous Downlink requires one satellite to maintain Line-of-Sight to both targets at once, yielding zero temporal delay but constrained geometry.Because no memory buffering occurs, decoherence is bypassed; requests without a suitable satellite are infeasible.
  • Satellite architectures: On-Orbit Stitching uses quantum memories to buffer qubits and perform asynchronous swapping, expanding feasible paths while incurring a coherence-time-dependent fidelity penalty.Routing becomes a memory-assisted path-selection problem over the dynamic graph.
  • Routing workloads: EASR consumes dynamic transmittance, visibility, contact-window, and memory-delay attributes to select feasible memory-assisted paths.Its objective combines spatial loss with temporal decoherence through a unified time-dependent edge weight.
  • Routing baselines: DSP minimizes instantaneous physical hops, whereas MPR greedily favors high-transmittance paths without explicitly modeling temporal memory decoherence.These baselines represent opposite simplifications in the routing decision space.
  • Routing workloads: EASR applies fidelity as a hard feasibility filter, then ranks surviving paths by an edge weight combining optical loss, swapping success, and storage cost.The algorithm estimates cumulative delay, prunes paths below the fidelity threshold, and returns the lowest-cost feasible path.

5 Software Architecture and Implementation

QUASAR provides software interfaces for instantiating dynamic LEO quantum network overlays, decoupling physical updates from discrete-event execution, and integrating custom routing algorithms. Its implementation addresses computationally expensive physical-layer evaluation while supporting spatiotemporal routing workloads.

  • 5 Software Architecture and Implementation: The software architecture exposes topology instantiation, event-driven physical decoupling, and custom routing orchestration as its three principal interfaces.These interfaces configure and execute QUASAR workloads.
  • 5.1 Spatiotemporal Topology Instantiation: QUASAR instantiates LEO constellations and ground stations from geographical coordinates and orbital specifications, then establishes dynamic quantum connections with time-varying attributes.Supported configurations include TLE datasets and Walker-Delta constellation descriptions.
  • 5.2 Event-Driven Physical Decoupling: Continuous environmental polling and static physical snapshots limit scalability because LEO quantum-network physical-layer evaluation is computationally expensive.The implementation identifies this physical-layer cost as a primary challenge for scalable evaluation.
  • 5.2 Event-Driven Physical Decoupling: QUASAR updates physical state only when visibility, range, or fidelity thresholds are crossed, decoupling spatial calculations from the network-layer event loop.The event-driven bridge triggers link or fidelity events and updates spatiotemporal edge weights when relevant conditions change.
  • 5.3 Custom Routing Orchestration: A unified RoutingAlgo interface lets researchers evaluate spatiotemporal heuristics without manually managing orbital and quantum-optical dynamics.The EASR reference workload combines spatial success probability with temporal decoherence in a unified edge weight and uses modified shortest-path search.

6 Case Study and System Evaluation

QUASAR is evaluated across controlled architectures, routing workloads, orbital traces, concurrent requests, and runtime mechanisms. The experiments show that it exposes physical dynamics to routing workloads while reducing network-layer update overhead through event-driven filtering.

  • The case study covers controlled architecture behavior, routing sensitivity, event-driven runtime overhead, trace-driven inputs, concurrent OD workloads, and mechanism ablation.
  • Controlled Mechanism Studies: Simultaneous Downlink produces sparse service instants, whereas On-Orbit Stitching creates wider EDR windows by buffering and stitching entanglement across contact intervals.
  • Controlled Mechanism Studies: QUASAR hosts DSP, MPR, and EASR workloads and exposes their differing sensitivity to quantum-memory coherence time under shared physical constraints.
  • Event-Driven Runtime Behavior: 85% lower network-layer update latency is achieved than continuous polling at the largest constellation setting through event-triggered updates, visibility pruning, and sparse graph exposure.
  • Trace-Driven and Concurrent Workloads: Starlink TLE inputs produce more irregular visibility dynamics than controlled Walker-Delta orbits, while QUASAR drives the same visibility, channel, and event pipeline.
  • Trace-Driven and Concurrent Workloads: Under 4 to 32 concurrent OD pairs, all workloads increase aggregate EDR, but DSP and MPR saturate earlier while EASR sustains higher aggregate EDR by accounting for spatial and temporal costs.
  • Runtime Mechanism Ablation: The full runtime configuration reduces normalized update frequency to 0.11× of polling and achieves a 10.2× latency speedup through combined event-driven mechanisms.

7 Conclusion

QUASAR provides a lightweight spatiotemporal simulator that translates continuous satellite quantum-network constraints into network-layer attributes for discrete-event routing evaluation. Its case study covers representative satellite architectures and workloads, achieving over 85% lower network-layer update latency than continuous polling.

  • QUASAR abstracts dynamic orbital topologies, time-varying optical transmittance, and quantum memory decoherence into network-layer attributes.
  • The simulator evaluates entanglement distribution in satellite quantum networks through a lightweight spatiotemporal architecture.
  • QUASAR models Simultaneous Downlink and On-Orbit Stitching while exercising an EDR-aware routing heuristic as a reference workload.
  • The case study evaluates architecture-dependent EDR, memory coherence, event composition, runtime scalability, orbital traces, concurrent workloads, and runtime ablation.
  • Over 85% lower network-layer update latency is achieved compared with continuous polling.
  • The results support QUASAR as a practical and extensible framework for future satellite quantum-network protocol evaluation.
Loading 2609.04920v1…