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 increasingly urgent due to the exploding demand for computationally intensive deep learning inference. These computations are typically offloaded to cloud computing servers, leading to vulnerabilities that can compromise the security of the clients' data. To solve this problem, we introduce a linear algebra engine that leverages the quantum nature of light for information-theoretically secure multiparty computation using only conventional 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 via the Holevo and the Cram\'er-Rao bounds, respectively. Applied to the MNIST classification task, we obtain test accuracies exceeding $96\%$ while leaking less than $0.1$ bits per weight symbol and $0.01$ bits per data symbol. This weight 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.

本研究解决了分布式用户隐私保护下的多方计算问题，尤其是在深度学习推理日益增长的需求背景下。我们提出了一种基于经典电信组件的线性代数引擎，用于信息理论上安全的多方计算，并在MNIST分类任务中实现了超过96%的测试准确率，同时泄露的信息量远低于进行深度学习所需的最低比特精度。这为实用的量子安全计算奠定了基础，推动了安全云深度学习的发展。

量子安全的多方深度学习