ArticleEntropy (Basel, Switzerland)2021
Federated Quantum Machine Learning.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 2 citations in OpenAlex.
- Federated Quantum Machine Learning over Satellite Networks: Toward Scalable Distributed Quantum Classification.Entropy (Basel, Switzerland) · 2026Article
- Strategic Data Re-Uploads: A Pathway to Improved Quantum Classification Data Re-Uploading Strategies for Improved Quantum Classifier Performance.Entropy (Basel, Switzerland) · 2026Article
- Alzheimer's detection using quanvolutional neural networks with federated training.Frontiers in neuroscience · 2026Article
- Experimentally Validated Quantum-Secure Federated Learning over a Multi-user Quantum Network.Research (Washington, D.C.) · 2026Article
- Multi-client distributed blind quantum computation with the Qline architecture.Nature communications · 2023Article
- Quantum architecture search via truly proximal policy optimization.Scientific reports · 2023Article
- Towards Secure Big Data Analysis via Fully Homomorphic Encryption Algorithms.Entropy (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federation setting yet. In this work, we present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model. Our distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training. It demonstrates a promising future research direction for scaling and privacy aspects.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.