Biometric authentication systems have come a long way in terms of accuracy and reliability, but they still suffer from computational efficiency and security issues constraints. This paper, examines the fusion of quantum computing with multimodal biometric authentication in order to improve the rate of recognition, the processing rate and it’s resistance to adversarial attacks. We work with quantum machine learning models like Variational Quantum Classifiers (VQC), and Quantum Neural Networks (QNN), to process fingerprint, iris, facial recognition datasets. Detailed comparison with conventional schemes indicates the advantage over the classical counterparts in terms of the authentication accuracy and the computation efficiency, respectively. Quantum-enhanced biometric security is verified by experimental results.
Biometric authentication systems have come a long way in terms of accuracy and reliability, but they still suffer from computational efficiency and security issues constraints. This paper, examines the fusion of quantum computing with multimodal biometric authentication in order to improve the rate of recognition, the processing rate and it’s resistance to adversarial attacks. We work with quantum machine learning models like Variational Quantum Classifiers (VQC), and Quantum Neural Networks (QNN), to process fingerprint, iris, facial recognition datasets. Detailed comparison with conventional schemes indicates the advantage over the classical counterparts in terms of the authentication accuracy and the computation efficiency, respectively. Quantum-enhanced biometric security is verified by experimental results.
Facial biometrics play a crucial role in identity verification, yet classical approaches face challenges related to computational complexity and security vulnerabilities. This paper explores the integration of quantum computing with eigenface-based face verification to enhance efficiency and security. By utilizing the advantages of Quantum Principal Component Analysis (QPCA), we achieve exponential speedups in eigenvalue decomposition, significantly reducing the computational burden of high-dimensional facial data processing. Our hybrid classical-quantum approach optimizes quantum state encoding and similarity measurement via the Swap Test techniques. Experimental results demonstrate improved verification accuracy and scalability compared to classical eigenfaces, particularly for large databases. Despite current hardware constraints, our findings establish a foundational framework for quantum-enhanced biometric systems. This work highlights the potential of quantum computing in facial recognition, and prepares the way for more efficient, secure, and scalable biometric authentication systems.
Face recognition systems are widely used for biometric authentication but face two major problems. First, processing high-resolution images and large databases requires extensive computational time. Second, emerging quantum computers threaten to break the encryption methods that protect stored facial templates. Quantum computers will soon be able to decrypt current security systems, putting biometric data at permanent risk since facial features cannot be changed like passwords. This paper presents a solution that uses quantum computing to speed up face recognition while adding quantum-resistant security. It applies quantum principal component analysis (QPCA) and the SWAP test to reduce the computational complexity and implement lattice-based cryptography, which quantum computers cannot break. Experimental evaluation demonstrates a significant overall speedup with improved accuracy. The proposed framework achieves a significant improvement in performance, provides 125-bit security against quantum attacks and compresses the data storage requirements significantly. These results demonstrate that quantum-enhanced face recognition can solve both the speed and security challenges facing current biometric systems, making it practical for real-world deployment as quantum technology advances.
Healthcare systems are rapidly becoming digital, which has increased the demand for safe, dependable, and privacy-preserving authentication methods. Iris biometrics have emerged as a key component for identity verification in delicate settings due to their high accuracy and stability. However, protecting privacy and data security is a difficulty for conventional biometric systems, particularly in light of new quantum risks. For advanced healthcare security, this study suggests a revolutionary quantum-enhanced, privacy-preserving iris biometrics system. Through the integration of quantum computing technologies, the framework makes use of quantum-accelerated algorithms like Grover's algorithm for efficient template matching and quantum-safe cryptography like Quantum Key Distribution (QKD). Homomorphic encryption and federated learning are two further ways to improve privacy preservation while guaranteeing adherence to HIPAA and other healthcare data protection laws. In comparison to conventional techniques, the suggested system performs better in terms of accuracy, security, and computational efficiency when tested on benchmark iris datasets. By filling important gaps in safe and privacy-focused identity management, this research creates a novel method at the nexus of quantum computing, biometrics, and healthcare.
Face recognition has become an essential biometric technology for applications such as security monitoring, user authentication, and intelligent surveillance systems. Conventional approaches based on deep neural networks, particularly convolution-based architectures, provide high recognition performance but often demand considerable computational resources and extended training durations as network complexity increases. To overcome these challenges, Quantum Machine Learning (QML) utilizes quantum computing concepts, including quantum superposition and entangled states, to achieve efficient feature representation and processing. This paper presents a Hybrid Quantum-Classical Face Recognition System built on a Multi-Gate Quantum Convolutional Neural Network (MG-QCNN). The proposed framework employs a 4-qubit variational quantum circuit that incorporates RY state encoding together with RX, RZ, and CNOT quantum operations to extract discriminative facial representations. In addition, a structural ablation analysis is conducted to investigate the influence of individual quantum components on the overall recognition capability of the model. To enhance spatial feature learning and classification performance, several classical neural network layers, namely convolution, batch normalization, max-pooling, dropout, and fully connected layers, are integrated after the quantum feature extraction stage. Within the hybrid framework, the quantum circuit is responsible for generating meaningful feature representations, whereas the classical neural network performs feature refinement and final identity classification. The framework is further combined with the DeepFace verification approach to perform facial similarity analysis and identity verification. Moreover, an Open-Set Face Recognition mechanism is incorporated to determine if an input facial image corresponds to a registered identity or represents an unseen individual. This capability increases system reliability and supports accurate unknown-person identification
Quantum computing is based on the principles of quantum mechanics and uses quantum bits (qubits) that can exist in a state of superposition and entanglement. They promise a revolution in computing and information security. Firstly, using quantum algorithms in information technology opens up new possibilities for processing and protecting data. Secondly, biometric information security relies on a person's unique physical and behavioural characteristics, such as fingerprints, facial recognition, iris, voice, and others. These methods provide a high level of security due to their uniqueness and difficulty in counterfeiting. Combining the principles of quantum computing and biometric information security allows us to identify their similarities and integration capabilities to create reliable systems for protecting confidential information. While previous research in biometric information security has focused on individual aspects of decision-making to ensure the information security of educational institutions when using facial recognition technologies, this work presents a comprehensive framework combining several components into a single unified system for using various quantum cryptography algorithms. The main advantages of using quantum computing in object and image detection include accelerating the computational process using quantum components, robustness at different object angles, suitability for moving or static conditions, cryptographic noise immunity, and the ability to work in real practical conditions. The advantages of the quantum cryptographic scheme for face recognition technologies include ease of implementation, high cryptography security, the ability to parallelise the processes of encoding and decoding images, and the fact that complex computer equipment is not required. The study results provide a solid foundation for developing hybrid quantum computing and biometric information security technologies. Using the possibility of hybrid computing opens up new horizons for the standardisation and public dissemination of new technologies, and such a combination ensures reliable protection of confidential information and stimulates further research aimed at improving data protection methods in the context of the rapid development of the digital world.
The rapid evolution of cyber threats has rendered conventional security approaches inadequate for managing increasingly sophisticated risks. This study introduces a Quantum Machine Learning Cybersecurity Framework that leverages quantum computing and machine learning to enhance cybersecurity across multiple dimensions. The research employs a structured methodology, beginning with the integration of Quantum Key Distribution (QKD) for secure key exchange and progressing through the deployment of Quantum Neural Networks (QNN) and Quantum Support Vector Machines (QSVM) for anomaly detection and adversarial threat management. The framework also incorporates Quantum Reinforcement Learning (QRL) for autonomous incident response, a Quantum Authentication module for securing identity verification using biometric and behavioral data, and a Policy Compliance Interface powered by Quantum Compliance Analyzers for regulatory adherence. Experimental results demonstrated substantial improvements in cybersecurity metrics, including a 96% accuracy in threat detection, a 28% reduction in incident response time, and a 96% success rate in compliance simulations. These findings underscore the framework's capacity to offer adaptive, scalable, and efficient cybersecurity solutions tailored to modern challenges. This study provides a significant step toward integrating quantum technologies into practical cybersecurity applications, paving the way for future innovations in intelligent, secure, and adaptable defense systems.