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Innovation in Quantum Computing for Cybersecurity Applications

Submission Deadline: 31 May 2026 (closed) View: 785 Submit to Special Issue

Guest Editor(s)

Dr. Manoj K. Jha

Email: manoj.jha@faculty.umgc.edu

Affiliation: Information Technology, University of Maryland Global Campus, Adelphi, 20783, United States

Homepage:

Research Interests: cybersecurity, quantum computing, artificial intelligence, machine learning, data science
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Summary

This Special Issue on "Innovation in Quantum Computing for Cybersecurity Applications" invites original research and review papers that explore emerging intersections of quantum theory, secure computation, and intelligent cyber defense. Topics of interest include quantum-inspired and post-quantum cryptographic methods, quantum-secure multi-party computation (Q-SMPC), quantum random number generation (QRNG), quantum-randomized anomaly detection (Q-RAD), and hybrid AI–quantum algorithms for threat intelligence and data protection. Contributions highlighting novel architecture, simulation frameworks, or experimental validations that advance quantum-level security, either with or without quantum hardware, are particularly encouraged.


This special issue welcomes original research articles and review articles. Research areas include (but are not limited to) the following topics:
· Hybrid AI–quantum algorithms for threat intelligence
· Simulation and experimental validation of quantum cybersecurity
· Quantum networking and secure communications


Keywords

quantum-inspired cybersecurity; post-quantum cryptography; quantum machine learning; quantum-resilient data privacy

Published Papers


  • Open Access

    ARTICLE

    Hybrid Quantum-Kernel and Quantum-Inspired Machine Learning for TDoS Early Warning in Critical Infrastructure

    Carlos Rosa-Remedios, Pino Caballero-Gil, Jezabel Molina-Gil
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.084352
    (This article belongs to the Special Issue: Innovation in Quantum Computing for Cybersecurity Applications)
    Abstract Increasing digitalization exposes critical infrastructure to sophisticated cyber threats, requiring new approaches to improving security and resilience. While classical machine learning techniques have shown promise in anomaly detection and threat mitigation, emerging quantum-inspired methods offer new opportunities to enhance detection capabilities by leveraging principles derived from quantum computing. The objective of this work is to propose a model for the early detection of Telephony Denial of Service attacks using a combination of classical algorithms and quantum computing-based techniques. Call records are embedded into a low-dimensional quantum feature space using spatial and temporal attributes, mapped through… More >

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