Distributed healthcare, cloud-enabled medical services, Internet of Medical Things (IoMT)-based technologies have greatly enhanced access to healthcare and real-time monitoring of patients, but the same technologies have created significant cybersecurity problems associated with unauthorized access, ransomware attacks, data interception, and sensitive patient information breaches. Secure communication in a distributed healthcare setting has become a substantial need, therefore, as conventional cryptography approaches are progressively becoming susceptible to novel and sophisticated cybercrimes as well as novel quantum computing assaults. The study presents a secure communication model, combining Quantum Key Distribution (QKD) based on the BB84 protocol and machine learning-based adaptive intrusion detection to distributed healthcare systems. The given framework uses BB84 quantum cryptography to create safe key exchange and encrypted healthcare communication networks, whereas the machine learning module would detect and classify malicious cyberattacks in real-time such as denial-of-service attacks, unauthorized access, malware, and network anomalies. The efficiency of the developed framework in the case of various attacks was evaluated experimentally on standard datasets on cybersecurity like NSL-KDD and CICIDS2017. Analysis of performance was conducted based on classification measures such as Accuracy, Precision, Recall, and F1-score as well as an assessment of attack vulnerability reduction and enhanced security in communication. As the results of the experiment show, the offered BB84 and machine learning-enhanced framework has a much greater intrusion detection capacity, leads to better secure healthcare communication, lower vulnerability to cyberattacks, and has a more solid defense against each eavesdropping and unauthorized intrusion than the use of traditional security mechanisms. The presented framework is a step towards scalable and intelligent healthcare cybersecurity systems, as it fuses quantum-safe communication with adaptive machine learning-based threat detection, as well as emphasizes the future potential of quantum artificial intelligence-based security architectures in the future of distributed healthcare infrastructure.
Distributed healthcare, cloud-enabled medical services, Internet of Medical Things (IoMT)-based technologies have greatly enhanced access to healthcare and real-time monitoring of patients, but the same technologies have created significant cybersecurity problems associated with unauthorized access, ransomware attacks, data interception, and sensitive patient information breaches. Secure communication in a distributed healthcare setting has become a substantial need, therefore, as conventional cryptography approaches are progressively becoming susceptible to novel and sophisticated cybercrimes as well as novel quantum computing assaults. The study presents a secure communication model, combining Quantum Key Distribution (QKD) based on the BB84 protocol and machine learning-based adaptive intrusion detection to distributed healthcare systems. The given framework uses BB84 quantum cryptography to create safe key exchange and encrypted healthcare communication networks, whereas the machine learning module would detect and classify malicious cyberattacks in real-time such as denial-of-service attacks, unauthorized access, malware, and network anomalies. The efficiency of the developed framework in the case of various attacks was evaluated experimentally on standard datasets on cybersecurity like NSL-KDD and CICIDS2017. Analysis of performance was conducted based on classification measures such as Accuracy, Precision, Recall, and F1-score as well as an assessment of attack vulnerability reduction and enhanced security in communication. As the results of the experiment show, the offered BB84 and machine learning-enhanced framework has a much greater intrusion detection capacity, leads to better secure healthcare communication, lower vulnerability to cyberattacks, and has a more solid defense against each eavesdropping and unauthorized intrusion than the use of traditional security mechanisms. The presented framework is a step towards scalable and intelligent healthcare cybersecurity systems, as it fuses quantum-safe communication with adaptive machine learning-based threat detection, as well as emphasizes the future potential of quantum artificial intelligence-based security architectures in the future of distributed healthcare infrastructure.
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.
The rapid advancement of quantum computing poses a significant threat to conventional cryptographic systems that rely on computational complexity for security [3]. Quan-tum Key Distribution (QKD) offers a secure communication mechanism based on the principles of quantum mechanics, enabling the detection of unauthorized interception attempts [1]. However, practical QKD deployments remain vulnerable to channel noise, hardware imperfections, and sophisticated eavesdropping attacks [5], [6]. This paper presents an AI-driven threat detection and response framework that combines BB84 Quantum Key Distribution, IBM Qiskit simulation, and machine learning-based anomaly detection [9]. A high-fidelity quantum simulator is developed using IBM Qiskit and AerSimulator to model realistic quantum communication channels with depo-larizing noise. A Random Forest classifier trained on 50,000 simulated transmission sequences identifies malicious activities by analyzing Quantum Bit Error Rate (QBER), noise levels, sifted key lengths, and engineered heuristic features [7]. Experimental analysis demonstrates high detection accuracy while maintaining real-time operational capabilities. The proposed system further incorporates automated mitigation techniques including Privacy Amplification and E91 protocol migration to enhance quantum network resilience [2]. The results demonstrate that integrating artificial intelligence with quantum cryptography significantly improves threat detection, response efficiency, and overall communication security.
Quantum Key Distribution (QKD) leverages quantum mechanics to achieve information-theoretic secure key ex-change, resilient to both classical and quantum computational attacks. This paper proposes a novel framework that integrates QKD with machine learning-based intrusion detection in Software-Defined Networks (SDNs). Using BB84 and BB92 protocols, we evaluated resilience under intercept-resend and photon number splitting attacks. Furthermore, Random Forest and XGBoost classifiers were enhanced with QKD-generated keys to secure intrusion detection pipelines. The results show improved accuracy (99.8%) and F1-scores (85.12%) compared to classical IDS approaches, highlighting the novelty of combining QKD with ML-driven cyber defense. The study validates performance against benchmark survey works, emphasizing the role of QKD in advancing future-ready cybersecurity architectures.
Using quantum cryptography can significantly support cybersecurity in healthcare, paving the way for safer digital environments. As the sector evolves, ongoing research and innovation in quantum technologies will be essential for ensuring the confidentiality and integrity of patient information, ultimately enhancing the quality of care delivered in an increasingly interconnected world. The chapter explores the evolution of quantum cryptography to strengthen cybersecurity in the healthcare sector. As reliance on digital technology increases, protecting sensitive patient information becomes more important. Quantum cryptography offers a new way to create secure communication using the principles of quantum mechanics. These briefs highlight the critical need for security measures that can prevent devastating cyber threats. The second section describes the role of quantum algorithms, particularly quantum key distribution (QKD) and quantum-resistant algorithms. QKD allows encryption keys to be exchanged securely, ensuring that any eavesdropping attempts are detected, thus preserving the integrity of communications. Quantum-resistant algorithms have also been designed to prevent quantum computer attacks that could compromise cryptographic systems. By integrating these technologies, healthcare organizations can reduce the negative impacts associated with traditional processes and protect patient information from unauthorized access to interventions and research reports. As these technologies continue to mature, their integration with healthcare systems provides an opportunity to build trust and cost-effectiveness in these digital health solutions. This integration not only increases data security but also supports the development of AI applications that can analyze large amounts of medical data. The digital environment is more secure. As the industry evolves, continued research and innovation in quantum technology will be necessary to ensure the confidentiality and integrity of patient data and ultimately improve the quality of care in an ever-changing world.
Because many modern healthcare security systems rely on traditional cryptographic primitives that are susceptible to quantum attacks, the quick development of quantum computing poses previously unheard-of risks. With the increasing use of wearable sensors, telemedicine, AI-driven diagnostics and distributed clinical databases, as well as the increasing digitisation of healthcare, the need for quantum-resilient protection becomes critical. The integration of quantum and post-quantum cryptography (PQC) as fundamental security measures for upcoming intelligent healthcare ecosystems is thoroughly examined in this chapter. We summarise recent developments in cryptographic standards, secure communication protocols, AI-enabled defence mechanisms and blockchain-based data integrity systems using a PRISMA-guided systematic review. With an emphasis on performance, scalability and resistance to Shor- and Grover-based attacks, we further develop a simulation framework to assess hybrid quantum, classical security architectures under adversarial quantum scenarios. Our results show that while hash-based signatures provide ultra-long-term integrity for genomic and archival medical data, lattice-based PQC schemes, in particular CRYSTALS-Kyber and Dilithium, show strong suitability for electronic health record (EHR) encryption, secure IoMT communication and federated learning aggregation. A unified quantum-resilient architecture incorporating PQC, blockchain consensus, AI-based anomaly detection and quantum key distribution (QKD) is one of the theoretical contributions. In summary, we highlight the potential of quantum homomorphic AI, neuromorphic cryptosystems, quantum biometrics and adaptive threat intelligence to develop a safe, reliable and scalable paradigm for quantum-ready healthcare.
The rapid advancement of quantum computing poses a significant threat to the confidentiality and integrity of classified and defense communications. Quantum algorithms have the potential to undermine widely deployed public key cryptographic schemes, exposing sensitive military and governmental information to interception and decryption. Foundational work in quantum cryptography has demonstrated that the principles of quantum mechanics can be leveraged to achieve fundamentally secure key distribution, offering a promising countermeasure to quantum-enabled attacks (Bennett & Brassard, 2014; Ekert, 1991). As defense communication systems increasingly rely on longterm data confidentiality, the urgency of transitioning toward quantum-resilient security architectures has become a strategic priority. Despite their widespread adoption, classical cryptographic mechanisms are increasingly vulnerable in the post-quantum era. Studies on post-quantum cryptography have shown that many conventional encryption and key exchange algorithms are susceptible to quantum adversaries, necessitating the development and standardization of new cryptographic primitives (Chen et al., 2016). Ongoing standardization efforts further highlight the complexity of migrating large-scale, mission-critical systems to quantum-safe solutions, particularly within defense environments where interoperability, reliability, and assurance requirements are stringent (Alagic et al., 2025). In parallel, artificial intelligence has emerged as a powerful enabler of advanced security analytics. Machine learning and deep learning techniques enhance intrusion detection, anomaly identification, and adaptive response capabilities within complex communication infrastructures (Buczak & Guven, 2016). Recent research also demonstrates that AI can be applied directly within quantum cryptographic systems, for example by improving signal processing, error correction, and performance optimization in quantum key distribution implementations (Chin et al., 2021). The convergence of quantum cryptography and AIdriven analytics therefore offers a complementary and proactive approach to securing defense communications against both classical and quantum threats. This paper analyzes the integration of quantum cryptography and artificial intelligence as a unified framework for protecting classified and defense communications. It synthesizes advances in quantum key distribution, post-quantum cryptography, and AI-based security analytics, and proposes an integrated perspective that enhances resilience, detection capability, and operational robustness. The study highlights key implications for defense communication systems, emphasizing how AI-enhanced quantum security architectures can support long-term confidentiality, adaptive defense, and strategic cyber resilience in the evolving threat landscape (Sarker et al., 2021).