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International Journal for Research in Applied Science and Engineering Technology

Quantum Key Voting System

Nallabothula Amarachinneswari

Published Apr 30, 2026• 0 citations
Publisher

Abstract

Recent research in 2024 emphasizes post- quantum security techniques to safeguard voting systems from future quantum-based attacks. At the same time, improvements in deep learning-based face authentication have enhanced the accuracy and reliability of voter verification under real-world conditions. Hybrid security models that combine cryptographic methods with face authentication provide stronger protection against unauthorized access and fraud. These systems focus on essential properties such as voter anonymity, data integrity, and end-to-end verifiability.However, challenges including high computational cost, scalability, and real-time implementation continue to be important areas for further research.Moreover, researchers are exploring optimized algorithms to reduce processing time and improve system efficiency. Efforts are also being made to enhance dataset management for accurate face storage and retrieval. Cloud-based implementations are being considered to support large-scale voting environments. These advancements indicate the growing importance of developing practical and scalable secure voting solutions

DOI
10.22214/ijraset.2026.81605

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Recent research in 2024 emphasizes post- quantum security techniques to safeguard voting systems from future quantum-based attacks. At the same time, improvements in deep learning-based face authentication have enhanced the accuracy and reliability of voter verification under real-world conditions. Hybrid security models that combine cryptographic methods with face authentication provide stronger protection against unauthorized access and fraud. These systems focus on essential properties such as voter anonymity, data integrity, and end-to-end verifiability.However, challenges including high computational cost, scalability, and real-time implementation continue to be important areas for further research.Moreover, researchers are exploring optimized algorithms to reduce processing time and improve system efficiency. Efforts are also being made to enhance dataset management for accurate face storage and retrieval. Cloud-based implementations are being considered to support large-scale voting environments. These advancements indicate the growing importance of developing practical and scalable secure voting solutions
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Ensuring the security and integrity of voting systems is crucial for maintaining democratic processes. With the rise of cyber threats targeting electronic voting, traditional cryptographic methods are proving inadequate in safeguarding against sophisticated attacks. Quantum cryptography, particularly Quantum Key Distribution (QKD), offers a promising solution to enhance the security of voting systems by leveraging the principles of quantum mechanics. This research explores the application of quantum cryptography in secure voting, highlighting its potential to address vulnerabilities in current systems. The study examines the technological, practical, and legal challenges of implementing quantum-based voting systems, including infrastructure requirements and public acceptance. Furthermore, it assesses how quantum cryptography ensures voter privacy, prevents tampering, and provides robust protection against future quantum computing threats. While significant hurdles remain, the integration of quantum cryptographic techniques presents a transformative opportunity to create more secure and transparent voting processes. This paper outlines the roadmap for adopting quantum cryptography in voting systems and its potential impact on electoral integrity.
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.
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Generation of secure communication was getting known as a promising technology called as a quantum cryptography. Unfortunately, it still faces several challenges, most notably in terms of high computational demands, scalability limitations, quantum decoherence, and vulnerability to side channel attacks, such that deployment of it in real world remains impossible yet. This study shows how Machine Learning (ML) and Deep Learning (DL) can be applied to tune the mentioned obstacles so that quantum cryptographic frameworks become more secure, more efficient and more scalable. In particular, we introduce a hybrid AI enabled model where RL can be utilized for tuning the performance of the post quantum cryptographic algorithm implementations, GANs can be adopted for measuring the robustness of the system, and FL can be used to make the quantum key distribution scalable. Besides, this thesis uses Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to incorporate the quantum authentication and key exchange methods. Additionally, our methodology employs techniques based on Graph Neural Networks (GNNs) to achieve the best performance in the networks and Adversarial Machine Learning (AML) to counter and detect, and then reduce cyber threats in the run time. For preferable cryptographic computations, we introduce Quantum Neural Networks (QNNs) to reduce its dependency on expensive quantum hardware. Experimental results also show that ML/DL based quantum security frameworks provide lowering of compute burden, improvement in real time security of data, and improve resistance to cyber threats. The result of this roadmap based on AI driven strategy is a comprehensive view, with a high level of direction describing the main steps to lead to post quantum security and take benefit of quantum cryptography and apply it in a multitude of significant utilization including secure communication, cryptographic financial systems or national infrastructure protection.
The emergence of quantum computing presents significant challenges to existing Identity and Access Management (IAM) systems, particularly concerning the security of current cryptographic algorithms. As quantum computers evolve, traditional public-key cryptography methods like RSA and ECC face increasing vulnerability, necessitating a transition to quantum-resistant alternatives. This document explores the critical intersection of post-quantum cryptography and IAM, examining the transformation required in security frameworks to maintain resilience in a quantum-enabled future. The discussion encompasses the evolution of quantum threats to current IAM implementations, the development of post-quantum cryptographic solutions, the essential transformation of IAM infrastructure, and the integration of advanced technologies, including artificial intelligence and quantum-safe authentication mechanisms. Special attention is given to practical implementation considerations, including hybrid authentication models and the modernization of Public Key Infrastructure (PKI) systems to ensure continued security in the post-quantum era.