“Adversarial Quantum-Clustering” A New Methodological Approach to Social Systems Modeling

Shashank Sheshar Singh, Sumit Kumar, Sunil Kumar Meena +3 more

Xi Huang, Wenfang Zhang, Shibin Zhang +1 more

Proximity-based mobile social networks (PMSNs) represent a novel category of social networks that enable mobile users to discover and engage in new social interactions with nearby individuals through user-profile matching. The user’s personal profile contains sensitive information and raises increasing privacy concerns. Numerous solutions have been proposed to address this ongoing challenge, but the current methods using classical cryptographic techniques may lack quantum resistance and fail to ensure long-term security due to the emergence of quantum computers. In this paper, we propose a quantum-secure privacy-preserving profile matching scheme for PMSNs. The proposed scheme is grounded in the quantum threshold private set intersection (QTPSI) scheme, leveraging principles from quantum physics to safeguard the privacy of user profile and ensure secure information transmission, even in the presence of an adversary with powerful quantum computing power. Compared to existing schemes, our approach integrates quantum technologies, including single photons, rotational encryption, and single-particle measurements, to facilitate profile matching. This methodology effectively resists both classical and quantum attacks, ensuring that all information related to the profiles and matching results remains confidential.

Walid El Maouaki, Alberto Marchisio, Taoufik Said +2 more

Recent advancements in quantum computing have led to the emergence of hybrid quantum neural networks, such as Quanvolutional Neural Networks (QuNNs), which integrate quantum and classical layers. While the susceptibility of classical neural networks to adversarial attacks is well-documented, the impact on QuNNs remains less understood. This study introduces RobQuNN, a new methodology to enhance the robustness of QuNNs against adversarial attacks, utilizing quantum circuit expressibility and entanglement capability alongside different adversarial strategies. Additionally, the study investigates the transferability of adversarial examples between classical and quantum models using RobQuNN, enhancing our understanding of cross-model vulnerabilities and pointing to new directions in quantum cybersecurity. The findings reveal that QuNNs exhibit up to 60% higher robustness compared to classical networks for the MNIST dataset, particularly at low levels of perturbation. This underscores the potential of quantum approaches in improving security defenses. In addition, RobQuNN revealed that QuNN does not exhibit enhanced resistance or susceptibility to cross-model adversarial examples regardless of the quantum circuit architecture.

Emmanuel E. Haven, Andrei Yu. Khrennikov, T. R. Robinson

Ahmad Alomari, S. B. Rathna Kumar

Zhong CHEN

Quantum computing introduces a transformative approach to social science research by integrating quantum probability models into social interaction experiments. This study examines the effects of quantum superposition, measurement-induced state collapse, and interference on decision-making processes. Compared to classical probability models, quantum approaches offer a more accurate representation of cognitive uncertainty, preference reversals, and contextual dependencies. The research highlights the advantages of quantum measurement in survey design, behavioral analysis, and policy simulations. However, challenges remain, including the limitations of current quantum hardware, the interdisciplinary gap between quantum mechanics and social sciences, and ethical concerns regarding data security and experimental manipulation. Future research should focus on refining quantum experimental methodologies, enhancing computational capabilities, and developing ethical guidelines for quantum-driven social science applications. By addressing these challenges, quantum computing can provide new theoretical insights and practical advancements in understanding human behavior and collective decision-making.