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Toward Implementation of Quantum-Secure Multiparty Deep Learning

Kfir Sulimany, Sivan Trajtenberg‐Mills, Ryan Hamerly, Yoann Piétri, Eleni Diamanti, Dirk Englund

Published Jan 1, 2026• 0 citations
Publisher

Abstract

Quantum-secure deep learning guaranties data security during distributed machine learning computations. We propose an architecture employing spectral filtering and homodyne detection for scalable, secure, cloud-based deep learning in optical networks.
DOI
10.1364/cleo_fs.2026.fm3a.4

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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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Quantum-Enhanced Cloud AI: The Next Frontier in Machine Learning and Deep Learning presents a scholarly and comprehensive examination of the convergence of quantum computing, cloud infrastructure, and advanced artificial intelligence. As classical computing approaches approach their practical and physical limits, this volume positions quantum-enhanced cloud platforms as a transformative foundation for next-generation machine learning and deep learning systems. The book systematically introduces core principles of quantum computing, cloud-based quantum architectures, and hybrid quantum–classical models, establishing a strong conceptual foundation before advancing to practical implementations. It critically examines quantum algorithms, error correction challenges, coherence limitations, and architectural considerations that shape the feasibility of Quantum AI. Through detailed case studies and comparative analyses, the book demonstrates how quantum machine learning techniques can outperform classical approaches in domains such as healthcare, bioinformatics, finance, cybersecurity, agriculture, smart cities, and Internet of Vehicles systems. Emerging topics including edge intelligence, quantum-enabled IoT, sustainable supply chains, and secure cloud-based healthcare frameworks are also explored. Written in a rigorous yet accessible style, this reference work serves graduate students, researchers, and professionals seeking to understand both the theoretical underpinnings and real-world implications of quantum-enhanced AI. It offers valuable insights into the technological, industrial, and societal impact of this rapidly evolving field. Key Features Comprehensive coverage of quantum computing principles and cloud-based AI architectures In-depth analysis of hybrid quantum–classcal machine learning models and algorithms Comparative evaluation of quantum and classical AI approaches across multiple application domains Practical insights into implementation challenges, scalability, and security Forward-looking perspectives on the future of Quantum AI and intelligent computing systems.
Quantum computing represents a major threat to future communication systems security: Once a certain level of maturity is reached, the security enjoyed by classical cryptographic schemes will immediately be put in check by (i) highly efficient quantum algorithms able to solve hard problems non-negligibly faster than the best-known classical solutions and (ii) potentially scalable fabrication techniques for quantum circuits. This work investigates the design of quantum-safe security primitives suitable for new cybersecurity threats in next-generation communications networks by means of the integrated use of quantum cryptography and deep learning. By combining the unconditionally secure guarantees offered by quantum cryptographic protocols with the adaptive, data-driven intelligence of deep learning models, this work proposes a holistic solution to secure communication infrastructures against not only current but also future cyber threats. A central element is the utilization of QKD-protocols that use the properties of quantum mechanics in order to securely distribute keys that are resistant to computational attacks. In parallel thereof, deep learning techniques are applied to monitor the system by analyzing real-time network traffic, identifying abnormal activities, and forecasting sophisticated attack patterns that frequently are overlooked by conventional security systems. This approach mitigates constraints of standalone quantum cryptography, including noise and hardware imperfections, and improves threat identification over traditional signature-based systems. The work studies the synergy between quantum cryptography and AI-assisted analytics, optimizing quantum channel estimation, error correction, and intrusion detection with deep learning. We conduct extensive simulations to show the efficacy of the framework against various adversarial attacks such as man in the middle, side-channel, and adversarial machine learning threats. The system has high stability without loss of quantum bit error rate, and high detection accuracy, for handling the change of attack vectors in distributed, heterogeneous communication networks.