NetSquid, a NETwork Simulator for QUantum Information using Discrete events

The development of quantum networks is essential for connecting distant quantum devices, paving the way for scalable distributed quantum computers and a quantum internet capable of performing tasks impossible with classical communication. Scaling up quantum computers to solve real-world problems will likely require interconnecting different modules. Quantum communication networks also enable unique applications. However, significant challenges remain, including the lack of precise requirements for constructing large-scale quantum networks. Existing analytical methods and ad-hoc simulations provide rough estimates, but lack the detail to fully capture the complex interplay between communicating devices and time-dependent behavior in even small-scale networks. Moreover, quantum networks require a well-integrated classical control plane to orchestrate quantum devices and manage entanglement distribution. The design of this control stack is complex, particularly given limitations such as the finite lifetimes of quantum memories. Furthermore, the requirements of most quantum network applications are still largely unknown. Analytical tools offer limited solutions; statistical methods are useful for simplified models but fall short for detailed studies; and information theory provides benchmarks but not specific performance predictions. The need for a more comprehensive simulation tool is therefore evident. Such a tool should possess accuracy in modeling relevant physics (including time-dependent noise), modularity allowing the combination of protocols and models, and scalability for large-network simulations. Classical network analysis employs network simulators to study complex emergent behaviors, but these simulators don't handle quantum behavior. While quantum simulators exist for monolithic quantum systems, simulating quantum networks presents a unique challenge: many devices communicate quantumly and classically, leading to complex stochastic and asynchronous behavior. A key advantage in network simulation is that the state space describing the quantum network's state at any time remains relatively small, unlike a quantum computation simulator. This paper introduces NetSquid to address this need.

The paper extensively reviews existing literature on quantum repeaters and their architectures, highlighting the limitations of analytical and ad-hoc simulation methods. It cites numerous works detailing different approaches to quantum repeaters and their performance analysis, including those using atomic ensembles, nitrogen-vacancy centers, and other technologies. The review emphasizes the need to move beyond simplified models to account for intricate details and time-dependent effects. Furthermore, the paper discusses existing classical network simulators and their inadequacy for simulating quantum behavior, and contrasts NetSquid's approach with other existing quantum network simulators like SimulaQron, QuNetSim, SQUANCH, QuISP, qkdX, and SeQUeNCe, highlighting NetSquid's strengths in terms of realistic physical models, time-dependent noise handling, modularity and scalability.

NetSquid is a Python-based software tool that uses a discrete-event simulation engine (PyDynAA) to simulate quantum networks. Simulation involves three steps: modeling the network using modular components and physical models; assigning protocols to network nodes; and executing the simulation for multiple runs to collect statistics. The modular framework allows for the representation of various hardware elements as components, which can be nested to create complex network setups. Quantum information is represented at the qubit level, with NetSquid internally tracking the shared quantum states to enable node-centric programming and seamless switching between different quantum state representations (ket vectors, density matrices, stabilizer tableaus, and graph states with local Cliffords). The discrete-event simulation efficiently handles time-dependent behavior and feedback loops, allowing accurate tracking of quantum state decoherence. Each simulation run consists of a sequence of random choices, generating different paths, and statistical analysis of many runs produces performance metrics. The modular design allows for easy substitution of components and protocols to investigate diverse scenarios.

The paper presents three key use cases demonstrating NetSquid's capabilities: 1. **Quantum Switch Simulation:** NetSquid simulated a quantum switch beyond analytically tractable regimes, including the impact of time-dependent memory noise. The results reproduced and extended previous analytical findings, demonstrating the ability to analyze larger parameter spaces and more realistic noise models. The simulation showed the capacity (number of produced GHZ states per second) as a function of buffer size and memory coherence time, and how it varies with different entanglement generation rates. 2. **Nitrogen-Vacancy Center Repeater Chain:** The simulation analyzed entanglement distribution over a long-distance repeater chain using nitrogen-vacancy (NV) centers in diamond. This use case showcased NetSquid's ability to handle detailed physical models and complex control plane logic, including asynchronous operations and unequal coherence times for communication and storage qubits. The simulation assessed the achievable distance, compared different protocols (SWAP-ASAP and NESTED-WITH-DISTILL), and performed sensitivity analysis on various hardware parameters. The results revealed that for near-term hardware, repeaters might not improve fidelity at shorter distances. For improved hardware, repeaters enhanced both rate and fidelity at longer distances. Sensitivity analysis highlighted the importance of detection probability for the SWAP-ASAP scheme. 3. **Atomic Ensemble Memory Comparison:** NetSquid efficiently compared two types of atomic-ensemble memories (atomic frequency combs and electronically induced transparency memories) for point-to-point entanglement generation. The modularity allowed for easy substitution of memory components without altering other parts of the simulation. Simulations of the BB84 quantum key distribution protocol revealed different performance characteristics depending on the distance, with EIT memories outperforming AFC memories at short distances and AFC outperforming EIT at longer distances. Furthermore, benchmarking results demonstrated NetSquid's scalability and efficiency. Simulations of the generation and measurement of GHZ states highlighted the exponential scaling of universal quantum computation formalisms (ket vectors, density matrices) versus the sub-exponential scaling of stabilizer formalisms (stabilizer tableaus, graph states with local Cliffords). Simulations of repeater chains with up to 1000 nodes showcased the efficient handling of events by NetSquid's discrete-event engine.

The results demonstrate NetSquid's significant contribution to the field of quantum network simulation. The tool's ability to handle realistic physical models, time-dependent noise, complex protocols, and large network scales allows for a more accurate and comprehensive assessment of quantum network designs. The findings from the use cases provide valuable insights into the performance trade-offs of different hardware and protocols, guiding future research and development efforts. The modularity of NetSquid makes it a powerful tool for exploring various designs and exploring the interplay between the physical layer and the classical control plane. The scalability demonstrated in the benchmarks suggests that NetSquid can handle significantly larger and more complex quantum networks than existing tools. The ability to compare different hardware platforms and protocols efficiently is a key advantage for accelerating the progress towards practical quantum networks.

NetSquid provides a powerful and versatile platform for simulating quantum networks and modular quantum computing architectures. Its modular design, accurate physical modeling capabilities, and efficient discrete-event engine enable simulations of complex networks with realistic noise models. The use cases presented in the paper demonstrated its ability to address critical challenges in the design and optimization of quantum networks. Future work could focus on enhancing the tool's capabilities with further advanced physical models, expanded protocol libraries, and improved performance optimizations. NetSquid offers valuable support for accelerating the development of practical and scalable quantum technologies.

While NetSquid addresses many challenges in quantum network simulation, some limitations remain. The computational cost of simulating large networks with highly detailed physical models can be substantial, even with parallelization. The accuracy of the simulations depends on the accuracy of the underlying physical models used. The current version of NetSquid is primarily designed for simulating networks based on qubits; other quantum systems may require additional extensions. Although the simulator offers a broad range of functionalities, some highly specific advanced protocols might require tailored implementations.