Quantum-Secure Multiparty Deep Learning
Kfir Sulimany, Sri Krishna Vadlamani, Ryan Hamerly, Prahlad Iyengar, Dirk Englund · Physical Review X · 2025
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.