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  • Open Access

Quantum-Secure Multiparty Deep Learning

Kfir Sulimany1,*, Sri Krishna Vadlamani1, Ryan Hamerly1,2, Prahlad Iyengar1,2, and Dirk Englund1

  • 1Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
  • 2Physics and Informatics Laboratories, NTT Research, Inc., Sunnyvale, California, USA

  • *Contact author: kfir@mit.edu
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Phys. Rev. X 15, 041056 – Published 24 December, 2025

DOI: https://doi.org/10.1103/k8wg-qmbh

Abstract

Secure multiparty computation enables the joint evaluation of multivariate functions across distributed users while ensuring the privacy of their local inputs. This field has become urgent due to the demand for computationally intensive deep learning inference. These computations are typically offloaded to cloud servers, leading to vulnerabilities. To solve this problem, we introduce a linear algebra engine that leverages the quantum nature of light for information-theoretically secure multiparty inference using telecommunication components. We apply this linear algebra engine to deep learning and derive rigorous upper bounds on the information leakage of both the deep neural network weights and the client’s data, enabling double-blind operations. Applied to the modified National Institute of Standards and Technology classification task, we obtain test accuracies exceeding 95% while guaranteeing leakage of less than 0.1 bits per weight and data element. This leakage is an order of magnitude below the minimum bit precision required for accurate deep learning using state-of-the-art quantization techniques. Our work lays the foundation for practical quantum-secure computation and unlocks secure cloud deep learning as a field.

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Popular Summary

The demand for secure computing is becoming increasingly urgent as more data-intensive tasks, like deep learning, are outsourced to cloud servers. This poses serious privacy risks, particularly for sensitive information in fields such as finance, health care, and business. Our research addresses the crucial question: Can we use the power of artificial intelligence (AI) and big data without compromising privacy? We discover that the answer is yes, thanks to our new approach that combines AI with the security advantages of quantum mechanics.

In our work, we developed a quantum-secure computation protocol that allows multiple parties to collaborate on complex AI computations while ensuring that neither proprietary data nor machine learning models get exposed to the other parties. Our protocol is powered by the quantum properties of light and is readily implemented using present-day photonics. We derive analytical upper bounds for information leakage of the data and models and demonstrate the protocol performance on benchmark tasks. Our solution also directly addresses the severe scaling limitations of classical secure-computation approaches.

Looking ahead, this quantum-secure deep learning approach opens the door to safer AI technologies for sectors where privacy is paramount. Future research could perform joint optimization of model parameters and protocol steps to further improve the accuracy and security. Furthermore, artificial intelligence-based tools can provide tighter upper bounds for information leakage. Lastly, integrating our secure computation protocol with quantum resources may pave the way to quantum computational advantages.

Article Text

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Phys. Rev. X 15, 041056– Published 24 December, 2025
  • Received 10 October 2024
  • Revised 9 August 2025
  • Accepted 31 October 2025
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