Cross-Validating Quantum Network Simulators

• URL: http://arxiv.org/abs/2504.01290v1
• Date: Wed, 02 Apr 2025 01:48:37 GMT
• Title: Cross-Validating Quantum Network Simulators
• Authors: Joaquin Chung, Michal Hajdušek, Naphan Benchasattabuse, Alexander Kolar, Ansh Singal, Kento Samuel Soon, Kentaro Teramoto, Allen Zang, Raj Kettimuthu, Rodney Van Meter,
• Abstract summary: We present a first cross-validation of two open-source quantum network simulators, QuISP and SeQUeNCe.<n>Our findings indicate that while the simulators differ in the time required to complete network tasks, they agree on the fidelity of the distributed resources under identical error parameters.
• Score: 33.695003310006456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a first cross-validation of two open-source quantum network simulators, QuISP and SeQUeNCe, focusing on basic networking tasks to ensure consistency and accuracy in simulation outputs. Despite very similar design objectives of both simulators, their differing underlying assumptions can lead to variations in simulation results. We highlight the discrepancies in how the two simulators handle connections, internal network node processing time, and classical communication, resulting in significant differences in the time required to perform basic network tasks such as elementary link generation and entanglement swapping. We devise common ground scenarios to compare both the time to complete resource distribution and the fidelity of the distributed resources. Our findings indicate that while the simulators differ in the time required to complete network tasks, a constant factor difference attributable to their respective connection models, they agree on the fidelity of the distributed resources under identical error parameters. This work demonstrates a crucial first step towards enhancing the reliability and reproducibility of quantum network simulations, as well as leading to full protocol development. Furthermore, our benchmarking methodology establishes a foundational set of tasks for the cross-validation of simulators to study future quantum networks.

Related papers

• Realistic quantum network simulation for experimental BBM92 key distribution [0.6640588568849828]
  We use a versatile discrete event quantum network simulator to simulate entanglement-based QKD protocol BBM92.<n>We simulate secure key rates in a repeater key distribution scenario for which no experimental implementations exist.
  arXiv  Detail & Related papers  (2025-05-30T17:49:00Z)
• Dissipation-driven quantum generative adversarial networks [11.833077116494929]
  We introduce a novel dissipation-driven quantum generative adversarial network (DQGAN) architecture specifically tailored for generating classical data. The classical data is encoded into the input qubits of the input layer via strong tailored dissipation processes. We extract both the generated data and the classification results by measuring the observables of the steady state of the output qubits.
  arXiv  Detail & Related papers  (2024-08-28T07:41:58Z)
• Simulators for Quantum Network Modelling: A Comprehensive Review [0.10742675209112622]
  We present a review of, to the best of our knowledge, currently used toolkits for modeling quantum networks. With these toolkits and standardized validation techniques, we can lay down the foundations for more accurate and reliable quantum network simulators.
  arXiv  Detail & Related papers  (2024-08-21T21:07:46Z)
• Digital versus Analog Transmissions for Federated Learning over Wireless Networks [91.20926827568053]
  We compare two effective communication schemes for wireless federated learning (FL) over resource-constrained networks. We first examine both digital and analog transmission methods, together with a unified and fair comparison scheme under practical constraints. A universal convergence analysis under various imperfections is established for FL performance evaluation in wireless networks.
  arXiv  Detail & Related papers  (2024-02-15T01:50:46Z)
• Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks [17.637761046608]
  Federated learning (FedL) distributes machine learning (ML) across worker devices by having them train local models that are periodically aggregated by a server. FedL ignores two important characteristics of contemporary wireless networks: (i) the network may contain heterogeneous communication/computation resources, and (ii) there may be significant overlaps in devices' local data distributions. We develop a novel optimization methodology that jointly accounts for these factors via intelligent device sampling complemented by device-to-device (D2D) offloading.
  arXiv  Detail & Related papers  (2023-11-07T21:17:59Z)
• Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Framework [70.83511997272457]
  We propose a semi-federated learning (SemiFL) paradigm to leverage both the base station (BS) and devices for a hybrid implementation of centralized learning (CL) and FL. We propose a two-stage algorithm to solve this intractable problem, in which we provide the closed-form solutions to the beamformers.
  arXiv  Detail & Related papers  (2023-10-04T03:32:39Z)
• Tensor Networks or Decision Diagrams? Guidelines for Classical Quantum Circuit Simulation [65.93830818469833]
  tensor networks and decision diagrams have independently been developed with differing perspectives, terminologies, and backgrounds in mind. We consider how these techniques approach classical quantum circuit simulation, and examine their (dis)similarities with regard to their most applicable abstraction level. We provide guidelines for when to better use tensor networks and when to better use decision diagrams in classical quantum circuit simulation.
  arXiv  Detail & Related papers  (2023-02-13T19:00:00Z)
• QSAN: A Near-term Achievable Quantum Self-Attention Network [73.15524926159702]
  Self-Attention Mechanism (SAM) is good at capturing the internal connections of features. A novel Quantum Self-Attention Network (QSAN) is proposed for image classification tasks on near-term quantum devices.
  arXiv  Detail & Related papers  (2022-07-14T12:22:51Z)
• Simulation Paths for Quantum Circuit Simulation with Decision Diagrams [72.03286471602073]
  We study the importance of the path that is chosen when simulating quantum circuits using decision diagrams. We propose an open-source framework that allows to investigate dedicated simulation paths.
  arXiv  Detail & Related papers  (2022-03-01T19:00:11Z)
• Parallel Simulation of Quantum Networks with Distributed Quantum State Management [56.24769206561207]
  We identify requirements for parallel simulation of quantum networks and develop the first parallel discrete event quantum network simulator. Our contributions include the design and development of a quantum state manager that maintains shared quantum information distributed across multiple processes. We release the parallel SeQUeNCe simulator as an open-source tool alongside the existing sequential version.
  arXiv  Detail & Related papers  (2021-11-06T16:51:17Z)
• Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation [24.084053136210027]
  We develop a sampling methodology based on graph sequential convolutional networks (GCNs) We find that our methodology while sampling less than 5% of all devices outperforms conventional federated learning (FedL) substantially both in terms of trained model accuracy and required resource utilization.
  arXiv  Detail & Related papers  (2021-01-04T05:59:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.