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Federating Quantum and Classical Computing: A Privacy-Preserving Hybrid Approach

Summary

A new arXiv paper evaluates federated learning with Sherpa.ai's Blind Vertical FL protocol to pair a hybrid quantum-classical active party with a classical passive party, reporting accuracy gains from 0.7227 to 0.8757 on a custom parity benchmark while using far fewer trainable parameters than classical baselines.

SourcearXiv Machine LearningAuthor: Carlos Cano, Daniel M. Jimenez-Gutierrez, Diego Sal, Georgios Kellaris, Joaquin del Rio, Oleksii Sliusarenko, Xabi Uribe-Etxebarria
Federating Quantum and Classical Computing: A Privacy-Preserving Hybrid Approach
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[Submitted on 18 Sep 2026]

Title:Federating Quantum and Classical Computing: A Privacy-Preserving Hybrid Approach

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Abstract:Quantum machine learning (QML) is increasingly recognized as one of the most promising near-term applications of quantum computing, viewed as a next-frontier candidate beyond purely classical approaches. Hybrid quantum-classical models operationalize this potential by embedding a parameterized quantum circuit within a model where all other components remain classical-a design already applied to chemistry simulation, financial modeling, and image classification. However, their deployment in privacy-sensitive, multi-party settings is constrained by the need to avoid centralizing raw data and by the requirement that modern quantum circuits remain parameter-efficient to stay trainable at scale.

In this paper, we address these constraints by evaluating federated learning (FL) as a means of combining a hybrid quantum-classical active party with a classical passive party, using this http URL's Blind Vertical FL (SBVFL) protocol to avoid centralizing raw data, while drastically reducing communication. We construct the split multiplicative periodic parity (SMPP) benchmark, following common QML design practice. On this task, our simulations show that SBVFL raises accuracy from 0.7227 to 0.8757 compared to local training, closely approaching non-private centralized accuracy, and that the hybrid quantum-classical model achieves this with substantially fewer trainable parameters than the classical neural networks and random forest alternatives. These results show that FL enables high-performing, privacy-preserving quantum-classical collaboration without centralizing raw data.

Subjects:

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)

Cite as: arXiv:2609.25082 [cs.LG]

(or arXiv:2609.25082v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.25082

arXiv-issued DOI via DataCite

Submission history

From: Joaquin Del Rio [view email] [v1] Fri, 18 Sep 2026 20:53:01 UTC (961 KB)

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Key points and analysis

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Key points

  • The work pairs a hybrid quantum-classical active party with a classical passive party via federated learning, avoiding any centralization of raw data.
  • Sherpa.ai's Blind Vertical FL (SBVFL) protocol is used to cut communication overhead drastically.
  • On the custom SMPP benchmark, accuracy rises from 0.7227 under local training to 0.8757, close to non-private centralized training.
  • The hybrid model reaches that accuracy with substantially fewer trainable parameters than classical neural networks or random forests.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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