A Review of Software for Designing and Operating Quantum Networks

Quantum network protocol development is crucial to realizing a production-grade network that can support distributed sensing, secure communication, and utility-scale quantum computation. However, the transition from laboratory demonstration to deployable networks requires software implementations of architectures and protocols tailored to the unique constraints of quantum systems. This paper reviews the current state of software implementations for quantum networks, organized around the three-plane abstraction of infrastructure, logical, and control/service planes. We cover software for both designing quantum network protocols (e.g., SeQUeNCe, QuISP, and NetSquid) and operating them, with a focus on essential control/service plane functions such as entanglement, topology, and resource management, in a proposed taxonomy. Our review highlights a persistent gap between theoretical protocol proposals and their realization in simulators or testbeds, particularly in dynamic topology and network management. We conclude by outlining open challenges and proposing a roadmap for developing scalable software architectures to enable hybrid, large-scale quantum networks.

💡 Research Summary

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The paper presents a comprehensive review of software tools for both designing and operating quantum networks, organized around a three‑plane abstraction—Infrastructure, Logical, and Control/Service—that mirrors classical networking architectures while respecting the unique constraints of quantum information.

In the introductory sections, the authors motivate the need for quantum networks to enable secure communication, distributed sensing, and large‑scale quantum computation. They note that current quantum networking efforts remain at the laboratory or metropolitan‑scale demonstration stage, and that transitioning to production‑grade networks requires robust software that can orchestrate heterogeneous quantum and classical components under stringent synchronization and error‑rate requirements.

Section 2 provides background on the fundamental quantum networking functions: entanglement generation (including Meet‑in‑the‑Middle and Midpoint‑Source protocols), entanglement swapping via quantum repeaters, and entanglement purification. The authors discuss the physical constraints such as photon loss, Hong‑Ou‑Mandel interference visibility, detector jitter, and the PLOB bound that limit raw entanglement rates, thereby justifying the need for sophisticated network‑level management.

Section 3 surveys the state‑of‑the‑art simulators used for protocol design and performance evaluation. The review is organized as a qualitative comparison (Table 1) and includes:

• SeQUeNCe – a Python‑based discrete‑event simulator offering modular resource, network, and entanglement managers, with flexible error‑model parametrization.
• QuISP – built on OMNeT++/C++, implements a full quantum‑repeater‑software‑architecture (QRSA) with connection manager, hardware monitor, rule engine, real‑time controller, and routing daemon, targeting large‑scale network studies.
• NetSquid – a Python API built on the pydynaa event engine, capable of simulating both quantum computation and networking stacks; although the core is open‑API, the distribution is currently closed‑source.
• QuantumSavory – a Julia framework focusing on physical‑layer dynamics, supporting stabilizer and state‑vector formalisms via external back‑ends.

Additional simulators with specialized goals (QuNetSim, SimQN, QKDNetSim, ReQuSim, QUreed, Quditto) and industry offerings (Aliro Simulator, QNetLab, Q Sim) are briefly described, highlighting their emphasis on modularity, no‑code interfaces, or high‑accuracy modeling. The authors note that while these tools excel at static topology analysis and protocol verification, they largely lack support for dynamic topology changes and real‑time resource orchestration.

Section 4 shifts focus to software that operates quantum networks in practice. Using the Control/Service plane, the authors propose a taxonomy of three core network functions and their sub‑functions:

1. Topology Management – discovery, failure detection, and dynamic reconfiguration.
2. Entanglement Management – generation, swapping, purification, and scheduling under tight timing constraints.
3. Resource Management – inventory of quantum memories, channel capacities, scheduling, and hardware abstraction.

Figure 4 illustrates this taxonomy, and Figure 5 emphasizes the need for disaggregated, interoperable components exposing standardized north‑bound and south‑bound APIs. The paper argues that each function should be implemented as an independent module with plug‑in capability, allowing protocol extensions without redesigning the entire stack.

Section 5 identifies open challenges:

• Dynamic topology and routing – current simulators assume static graphs; real networks will need adaptive routing that accounts for link failures and varying entanglement rates.
• Standardized APIs and abstraction layers – a common north‑bound interface for applications and a south‑bound interface for hardware are missing, hindering interoperability.
• Bridging simulation fidelity and test‑bed realism – discrepancies between idealized error models and hardware‑specific noise must be reconciled.
• Service‑level agreements for entanglement distribution – defining QoS metrics (fidelity, latency, throughput) and guaranteeing them across heterogeneous nodes is an open problem.
• Sustainable open‑source ecosystem – fostering community contributions and long‑term maintenance is essential for scaling the software stack.

The authors propose a roadmap: first, define a minimal set of standardized APIs (north‑bound for applications, south‑bound for hardware) and a plug‑in architecture for protocol modules. Second, develop prototype implementations of dynamic topology management and real‑time resource schedulers, integrating them into existing simulators (e.g., extending SeQUeNCe or QuISP). Third, validate these prototypes on emerging test‑beds that combine photonic hardware, quantum memories, and classical control processors. Finally, iterate toward a production‑grade, hybrid quantum‑classical network stack capable of supporting multiple services such as QKD, distributed sensing, and cloud‑based quantum computing.

In conclusion, the review highlights a significant gap between theoretical quantum network protocols and their practical software realization, especially in the control and service plane. By introducing a clear three‑plane abstraction and a detailed taxonomy of network functions, the paper provides a common language for future research and development. The proposed roadmap aims to guide the community toward scalable, interoperable, and dynamically managed quantum networks, paving the way for the transition from laboratory demonstrations to real‑world quantum internet infrastructures.