SimQFL: A Quantum Federated Learning Simulator with Real-Time Visualization
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Abstract (arXiv)
Quantum federated learning (QFL) is an emerging field that has the potential to revolutionize computation by taking advantage of quantum physics concepts in a distributed machine learning (ML) environment. However, the majority of available quantum simulators are primarily built for general quantum circuit simulation and do not include integrated support for machine learning tasks such as training, evaluation, and iterative optimization. Furthermore, designing and assessing quantum learning algorithms is still a difficult and resource-intensive task. Real-time updates are essential for observing model convergence, debugging quantum circuits, and making conscious choices during training with the use of limited resources. Furthermore, most current simulators fail to support the integration of user-specific data for training purposes, undermining the main purpose of using a simulator. In this study, we introduce SimQFL, a customized simulator that simplifies and accelerates QFL experiments in quantum network applications. SimQFL supports real-time, epoch-wise output development and visualization, allowing researchers to monitor the process of learning across each training round. Furthermore, SimQFL offers an intuitive and visually appealing interface that facilitates ease of use and seamless execution. Users can customize key variables such as the number of epochs, learning rates, number of clients, and quantum hyperparameters such as qubits and quantum layers, making the simulator suitable for various QFL applications. The system gives immediate feedback following each epoch by showing intermediate outcomes and dynamically illustrating learning curves. SimQFL is a practical and interactive platform enabling academics and developers to prototype, analyze, and tune quantum neural networks with greater transparency and control in distributed quantum networks.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 15 of its 27 references that are in the map. First posted on arXiv 17 Aug 2025.
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15 of 27 references.
References · 15
- 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
- 2024A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerancecited by 32
- 2023Foundations of Quantum Federated Learning Over Classical and Quantum Networkscited by 21
- 2023Towards Quantum Federated Learningcited by 20
- 2024FedQNN: Federated Learning using Quantum Neural Networkscited by 20
- 2022Non-IID Quantum Federated Learning with One-shot Communication Complexitycited by 15
- 2025Quantum Federated Learning for Multimodal Data: A Modality-Agnostic Approachcited by 8
- 2025Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noisecited by 8
- 2024Quantum Federated Learning Experiments in the Cloud with Data Encodingcited by 7
- 2024MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryptioncited by 7
- 2025Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learningcited by 7
- 2024Electrocardiogram (ECG) Based Cardiac Arrhythmia Detection and Classification using Machine Learning Algorithmscited by 4
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Citing papers per year: 2025: 2.
2 papers.