SimQFL: A Quantum Federated Learning Simulator with Real-Time Visualization

2025-08-17

AI Summary

Current quantum simulators are designed for generic quantum circuit simulation and lack native support for quantum machine learning—particularly quantum federated learning—making it difficult to integrate user data, monitor training in real time, or debug quantum circuits. To address this gap, we propose the first configurable simulation platform specifically tailored for quantum federated learning, integrating quantum circuit simulation, a federated learning framework, and a dynamic visualization engine. The platform supports customizable inputs—including client data, number of clients, qubit count, circuit depth, and hyperparameters—and introduces novel real-time per-round learning curve visualization and model convergence tracking. This design significantly improves prototyping efficiency and experimental transparency for quantum neural networks in distributed settings, while reducing resource overhead and implementation complexity in algorithm validation.

Technology Category

• Machine Learning: Quantum Machine Learning
• Humans and AI: Human-in-the-loop Machine Learning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Lack of quantum simulators supporting machine learning tasks
• Difficulty in designing and assessing quantum learning algorithms
• Absence of real-time updates and user-specific data integration

Innovation

• Real-time QFL visualization and monitoring
• Customizable quantum and ML parameters
• Interactive quantum neural network prototyping