An Individual in the present time is surrounded with advanced computing technology in several forms. It is expected that in the very near future, Quantum Computers will be replacing the fastest computer systems of present times and will be having enough computational power to bring down the most prevailing cryptographic algorithm. The information that is being shared between different users on the Internet will be at risk in that particular scenario even after significant changes, like increasing the key size or using more complicated mathematical calculations for encrypting the text, which won’t be able to protect the integrity of the system. Several types of research are being carried out to protect the data and make systems resilient to the assaults made by the Quantum Computers of the Future. In this Research, a dynamic type of cryptography is discussed that has a lot of potential to overcome this situation in Post Quantum Scenario, namely Adversarial Neural Cryptography. Adversarial Neural Cryptography works on the architecture of Generative Adversarial Networks where two Neural Networks try to communicate with each other and protect the communication channel and a third Neural Network tries to compromise the connection. The conducted research also explores the computing applicability of the presented notion and its effect in the post-quantum Scenario.
An Individual in the present time is surrounded with advanced computing technology in several forms. It is expected that in the very near future, Quantum Computers will be replacing the fastest computer systems of present times and will be having enough computational power to bring down the most prevailing cryptographic algorithm. The information that is being shared between different users on the Internet will be at risk in that particular scenario even after significant changes, like increasing the key size or using more complicated mathematical calculations for encrypting the text, which won’t be able to protect the integrity of the system. Several types of research are being carried out to protect the data and make systems resilient to the assaults made by the Quantum Computers of the Future. In this Research, a dynamic type of cryptography is discussed that has a lot of potential to overcome this situation in Post Quantum Scenario, namely Adversarial Neural Cryptography. Adversarial Neural Cryptography works on the architecture of Generative Adversarial Networks where two Neural Networks try to communicate with each other and protect the communication channel and a third Neural Network tries to compromise the connection. The conducted research also explores the computing applicability of the presented notion and its effect in the post-quantum Scenario.
Abstract It is viewed as revolutionary technology, offering incredible potential in terms of computing power within different scientific and industrial sectors. Quantum computers provide remarkable computing power; however, they also present significant security risks to existing public-key cryptography systems used to secure digital communication, online banking systems, government, medical applications, cloud computing systems, and IoT systems. Security of commonly used encryption schemes like RSA, DH, and ECC depends on mathematical problems that are impossible for classical computers to solve but can be easily resolved with the help of powerful enough quantum computers by using Shor’s algorithm. Furthermore, Grover’s algorithm makes exhaustive search attacks much faster, thus effectively lowering the security level of symmetric encryption schemes since it provides the speed-up for brute-force attacks. Post-Quantum Cryptography (PQC) turned out to be an effective and scalable method to protect digital data from attacks using quantum computing. In contrast to quantum cryptography, which requires a special quantum communication channel, PQC uses classical cryptographic algorithms that cannot be broken down by either classical or quantum computer due to certain mathematical problems. This research paper offers an extensive exploration of post-quantum cryptography through the analysis of the development of quantum computing, weaknesses of current cryptographic techniques, and mathematics behind quantum-resistant algorithms. Moreover, this paper explores the major classes of PQC techniques such as lattice-based, code-based, hash-based, multivariate, and isogeny-based cryptography and provides details about their security and application. Moreover, the report gives an overview of the NIST Post-Quantum Cryptography Standardization Project and analyzes recent standardizations of quantum-proof algorithms to be utilized in future digital infrastructures. Keywords: Post-Quantum Encryption, Quantum Security, Quantum-Proof Public-Key Cryptography, Lattice-Based Security Protocols, ML-KEM Key Encapsulation Mechanism, ML-DSA Digital Signature Algorithm, NIST Post-Quantum Cryptography, Contemporary Cybersecurity, Secure Communications, Cryptographic Algorithms.
Quantum computing is a new technology that may result in a revolution in the way data is transferred around the globe. However, it also has quite serious risks for existing cryptographic systems and thus, many encryption methods are insecure against quantum attacks. Future security frameworks will incorporate advanced quantum cryptography to overcome these issues. The use of post-quantum algorithms post-quantum algorithms and quantum key distribution quantum key distribution based on their fundamental concepts are strong defenses, according to quantum cryptography against quantum adversaries. Quantum cryptography is also demonstrated in its application to practical scenarios in important sectors such as infrastructure, healthcare, banking, and defense. Quantum cryptography and its potential to improve the global cybersecurity ecosystems through the integration of cutting-edge research and feasible methods of implementation are presented. It also describes the functions of quantum cryptography in securing private communications, maintaining data reliability, and handling the peculiar challenges of quantum threats. This work also focuses on how quantum cryptography is transforming security concepts and how this transformation is necessary to ensure the future of communication networks. The study reveals how quantum cryptography can transform the way secure communications are established and form the foundation of the digital society through the integration of theoretical ideas and practical applications.
The rapid advancement of quantum computing poses a significant threat to traditional cryptographic systems that rely on computational hardness assumptions such as integer factorization and discrete logarithms. Quantum algorithms, particularly Shor's algorithm, have the potential to break widely used cryptographic standards including RSA and Elliptic Curve Cryptography. To address these emerging challenges, this research proposes a machine learning-enhanced post-quantum cryptographic framework designed to provide adaptive and resilient data security in the quantum computing era. The proposed framework integrates post-quantum cryptographic algorithms with intelligent machine learning mechanisms to improve the robustness of encryption systems against quantum-based attacks. The system incorporates three major components: a post-quantum encryption module, a machine learning–based anomaly detection module, and an adaptive key management system powered by reinforcement learning. The anomaly detection component utilizes neural network models to monitor encrypted data streams and identify abnormal patterns that may indicate potential decryption attempts. In addition, reinforcement learning dynamically adjusts cryptographic key parameters and rotation schedules to reduce the risk of key compromise. Experimental evaluation demonstrates that the proposed framework significantly improves detection accuracy, reduces key management latency, and enhances resilience against simulated quantum decryption attacks when compared with conventional cryptographic systems. The integration of machine learning enables the framework to dynamically adapt to evolving threats while maintaining strong encryption standards. The results highlight the potential of combining artificial intelligence with post-quantum cryptography to build adaptive, intelligent, and future-proof security systems capable of protecting sensitive data in the presence of powerful quantum computing technologies.\\n\\n
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.
With the rapid advancement of artificial intelligence (AI) and quantum computing, cybersecurity threats have evolved, giving rise to adversarial quantum attacks. These attacks exploit the vulnerabilities of AI models using quantum algorithms, posing a significant risk to data security, model robustness, and decision-making systems. This paper presents a comprehensive review of adversarial quantum attacks on AI, analyzing their mechanisms, potential impacts, and countermeasures. It explores how quantum computing can enhance adversarial attacks by accelerating the generation of adversarial examples, breaking cryptographic protections, and undermining AI model integrity. Additionally, the study examines different attack vectors, including quantum-enhanced adversarial perturbations, quantum machine learning (QML) vulnerabilities, and quantum decryption threats. The paper also discusses defensive strategies such as quantum-resistant AI models, quantum cryptographic defenses, and hybrid quantum-classical security frameworks that can mitigate these risks. By evaluating existing research and emerging trends, this review provides insights into the growing intersection of AI security and quantum computing. The findings emphasize the urgent need for robust quantum-aware AI security frameworks to safeguard AI-driven systems in the quantum era. Future research directions include developing quantum-adaptive AI models, post-quantum cryptographic techniques, and real-world applications of quantum-safe AI architectures. This review aims to contribute to the ongoing discourse on ensuring AI resilience against adversarial quantum threats, paving the way for a secure and quantum-resistant future.
Several studies have shown that quantum neural networks are susceptible to adversarial attacks, which pose a significant threat to the accuracy of quantum neural networks. Adversarial attacks may exploit the input data of a network by feeding incorrect data to a model while manipulating one that has already been trained, which may cause erroneous results. In this research, we propose a novel defense model to protect quantum neural networks against adversarial attacks by using homomorphic data encryption. Homomorphic encryption allows computations to be processed on encrypted data that has not yet been decrypted by converting the data into ciphertext, which allows the data to be handled in its original form without any risks of privacy breach. By incorporating homomorphic data encryption into quantum neural networks, our proposed model tries to reduce adversarial attacks that may perturb network outputs. Taken together, our approach sheds light on the future of quantum computing and the preservation of sensitive information.