As quantum machine learning (QML) continues to evolve, it promises unparalleled computational advantages in processing complex data. However, the rise of QML also introduces critical concerns regarding data security and privacy, particularly in sensitive domains such as healthcare, finance, and defense. Classical cryptographic methods fall short in addressing threats that arise in quantum communication and computation environments. To bridge this gap, this paper presents a hybrid framework that integrates quantum cryptography—specifically Quantum Key Distribution (QKD)—with QML pipelines, ensuring end-to-end quantum-secure machinelearning operations. We first construct a theoretical model that outlines how QKD can be effectively embedded into a typical QML workflow to mitigate adversarial threats such as eavesdropping, model inversion, and poisoning attacks. We then implement this framework using IBM’s Qiskit and a simulated QKD environment via QuNetSim, applying it to a quantum support vector machine (qSVM) classifier. The integration is evaluated based on accuracy, computational overhead, and communication latency. Our results indicate that quantum-secured QML systems can maintain robust model performance while significantly enhancing data confidentiality. This work lays the groundwork for future developments in secure quantum artificial intelligence infrastructures.
As quantum machine learning (QML) continues to evolve, it promises unparalleled computational advantages in processing complex data. However, the rise of QML also introduces critical concerns regarding data security and privacy, particularly in sensitive domains such as healthcare, finance, and defense. Classical cryptographic methods fall short in addressing threats that arise in quantum communication and computation environments. To bridge this gap, this paper presents a hybrid framework that integrates quantum cryptography—specifically Quantum Key Distribution (QKD)—with QML pipelines, ensuring end-to-end quantum-secure machinelearning operations. We first construct a theoretical model that outlines how QKD can be effectively embedded into a typical QML workflow to mitigate adversarial threats such as eavesdropping, model inversion, and poisoning attacks. We then implement this framework using IBM’s Qiskit and a simulated QKD environment via QuNetSim, applying it to a quantum support vector machine (qSVM) classifier. The integration is evaluated based on accuracy, computational overhead, and communication latency. Our results indicate that quantum-secured QML systems can maintain robust model performance while significantly enhancing data confidentiality. This work lays the groundwork for future developments in secure quantum artificial intelligence infrastructures.
Quantum cryptography has emerged as a radical research field aimed at mitigating various security threats in modern communication systems. The integration of Quantum Machine Learning (QML) protocols plays a crucial role in enhancing security measures, addressing previously inaccessible threats, and improving cryptographic efficiency. Key research areas in quantum cryptography include Quantum Key Distribution (QKD), eavesdropping detection, QSDC, security analysis of QKD protocols, post-quantum cryptography, Quantum Network Security & Intrusion Detection, Quantum-secure communication beyond QKD, quantum random number generation, Quantum Secure Multi-Party Computation (QSMPC), Quantum Homomorphic Encryption (QHE), and privacy-preserving computation. QML algorithms improve the key generation of QKD, by improving quantum state selection and reducing measurements. This also allows them to increase efficiency because it identifies trends in errors and applies corrections, making quantum cryptography a more dependable option. With intelligent processing machine learning is excellent at handling complex, high-dimensional data-this may provide a viable strategy for enhancing QKD performance and increasingly real-world secure quantum communication networks. This review will explore current research gaps and future developments in QKD, security analysis of QKD protocols, and eavesdropping detection by leveraging various QML algorithms.
Abstract The rapid expansion of Internet of Things (IoT) infrastructures has heightened their exposure to sophisticated adversaries, including those equipped with quantum capabilities. This work introduces a hybrid framework that integrates post-quantum cryptography (PQC), quantum key distribution (QKD), and quantum machine learning (QML) to deliver quantum-resilient anomaly detection. A novel PQC-IoT dataset is generated using real-time Raspberry Pi sensors, enriched with 30+ features spanning 12 traffic classes (9 attacks, 3 benign), and incorporating PQC-specific metadata such as encryption latency and ciphertext size. Security is enforced through Kyber-512 encryption and a BB84-based QKD channel, while anomaly detection is performed using a quantum support vector machine (qSVM). Experimental results show that qSVM consistently outperforms classical SVM and XGBoost by 4--7% in accuracy, precision, recall, and F1-score, achieving up to 97.6% accuracy with encryption overhead limited to 5--7%. The framework maintains a Quantum Bit Error Rate (QBER) below 11% under noise models, validating its robustness. To the best of our knowledge, this is the first reproducible benchmark that unifies PQC, QKD, and QML into a single pipeline, offering a practical pathway toward quantum-secure IoT anomaly detection.
The rise of quantum computing poses threats to traditional cryptographic systems, thereby requiring security measures to safeguard against traditional and quantum-attacker cyber insecurity. This framework uses quantum machine learning (QML) algorithms along with quantum-safe encryption to boost security measures. The proposed system combines QML anomaly detection models variational quantum classifier (VQC) with quantum support vector classifier (QSVC) as well as quantum neural network and examines them based on the BB84 QKD protocol for information safety. This model was evaluated using three datasets of HIKARI Flow intrusion detection records, phishing activity logs, and malicious URLs. This includes all high-dimensional input by extensive application of feature engineering, which merges entropy scoring combined with keyword extraction and domain analysis to transmogrify it into suitable inputs for quantum processing. The QML models outperformed the traditional models with a maximum phishing detection accuracy of 97.75% by QSVC implementation. With the BB84 protocol, its eavesdropping detection was proved by quantum interference detection upon testing on IBM's Qiskit and Google's Cirq systems while the operations were secure and in attack scenarios. This system combines the latest features to address the limitations of the current AI security model and incorporates post-quantum cryptography to protect against quantum threats. In conclusion, QML and quantum cryptography work efficiently with operational cybersecurity platforms. Received: 31 December 2024 | Revised: 10 April 2025 | Accepted: 7 May 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Ramasubramaniyan Gunasridharan: Methodology. Ali Altalbe: Methodology. Bharathi Mohan Gurusamy: Conceptualization, Writing - review & editing. Gundala Pallavi: Writing - original draft. Prasanna Kumar Rangarajan: Supervision.
As artificial intelligence (AI) systems increasingly underpin critical applications in healthcare, finance, defense, and infrastructure, ensuring their security has become paramount. However, the rapid advancement of quantum computing poses a significant threat to classical cryptographic schemes that currently safeguard these systems. Quantum algorithms such as Shor’s and Grover’s threaten to undermine the confidentiality, integrity, and authentication of data and models that AI systems depend on. In this context, Post-Quantum Cryptography (PQC) has emerged as a crucial area of research aimed at developing cryptographic algorithms that are secure against both classical and quantum adversaries. This paper investigates the intersection of quantum computing threats and AI system vulnerabilities, highlighting how current security mechanisms may fail in a post-quantum era. We explore various categories of PQC—including lattice-based, hash-based, and multivariate polynomial cryptography—and evaluate their applicability to securing AI systems, particularly in scenarios such as model distribution, federated learning, data encryption, and communication in AI-enabled edge and IoT environments. Through analysis of emerging use cases and proposed architectures, the paper outlines how PQC can be integrated into the AI lifecycle to mitigate quantum-era threats. It also identifies key challenges such as computational overhead, integration complexity, and standardization gaps. The study concludes by outlining future research opportunities, including the development of efficient PQC protocols tailored to AI workloads, the creation of quantum-resilient AI pipelines, and the importance of interdisciplinary collaboration to secure AI’s future.
Secure, privacy-preserving sharing and analysis of health data is foundational to precision medicine, population health, and telehealth services. Emerging quantum networks promise fundamentally new communication primitives (entanglement, quantum key distribution) that when integrated with advanced AI architectures can enable end-to-end secure healthcare systems with improved confidentiality, tamper resistance, and novel distributed computation modes. This article develops a systematic framework for integrating quantum communications and cryptography with classical and hybrid quantum–classical AI models in healthcare. We (a) summarize the enabling quantum network technologies and AI building blocks, (b) propose layered system architectures and protocols for secure data exchange and collaborative learning, (c) formalize threat models and privacy requirements in healthcare contexts, (d) discuss algorithmic integrations (federated learning with QKD, quantum-enhanced machine learning, secure multiparty quantum protocols), (e) provide evaluation metrics and prototype deployment roadmaps, and (f) identify research gaps and regulatory implications. The treatment is scholarly yet practical, aimed at researchers, engineers, and healthcare stakeholders planning future-proof secure AI systems. (Keywords: quantum networks; quantum key distribution; quantum machine learning; federated learning; healthcare security; privacy.)
The growing complexity and velocity of cyber threats in high-security environments such as defense, critical infrastructure, and intelligence networks necessitates a paradigm shift in threat detection capabilities. Traditional cybersecurity systems, including those enhanced by classical machine learning algorithms, often struggle to process and classify massive volumes of heterogeneous and encrypted data in real time. This shortcoming is particularly evident in the context of advanced persistent threats (APTs), polymorphic malware, and insider attacks, which require rapid adaptation and heightened sensitivity to anomalous behavior.Quantum Machine Learning (QML), an emerging interdisciplinary field at the intersection of quantum computing and artificial intelligence, presents a promising avenue for augmenting threat detection mechanisms. Leveraging quantum phenomena such as superposition and entanglement, QML models offer potential advantages in processing speed, pattern recognition, and feature space transformation that can outperform their classical counterparts in high-dimensional data analysis. This paper explores the application of QML to threat detection in high-security networks, proposing a hybrid quantum-classical framework that integrates quantum-enhanced classifiers such as quantum support vector machines and variational quantum circuits into existing detection pipelines.The study outlines a technical overview of quantum computing principles relevant to cybersecurity, critically evaluates existing detection architectures, and presents simulation-based case studies to assess performance metrics, including detection accuracy and false positive rates. It further examines the limitations of current quantum hardware, algorithmic constraints, and emerging ethical and operational considerations. The findings suggest that while QML is still constrained by hardware maturity and integration complexity, it holds transformative potential for proactive, intelligent, and adaptive cyber defense systems in high-stakes environments. This research contributes to ongoing efforts to future-proof cybersecurity infrastructure against both classical and post-quantum threat landscapes.