Quantum computing and machine learning (ML) have received significant developments which have set the stage for the next frontier of creative work and usefulness. This paper aims at reviewing various data-encoding techniques in Quantum Machine Learning (QML) while highlighting their significance in transforming classical data into quantum systems. We analyze basis, amplitude, angle, and other high-level encodings in depth to demonstrate how various strategies affect encoding improvements in quantum algorithms. However, they identify major problems with encoding in the framework of QML, including scalability, computational burden, and noise. Future directions for research outline these challenges, aiming to enhance the excellence of encoding techniques in the constantly evolving quantum technology setting. This review shall enable the researcher to gain an enhanced understanding of data encoding in QML, and it also suggests solutions to the current limitations in this area.

Recently, the Digital Twin (DT) technology has procured a lot of attention because of its applicability in the manufacturing and space industries. The DT environment involves the formation of a clone of the tangible object to perform simulations in the virtual space. The combination of conceptual development, predictive maintenance, real-time monitoring, and simulation characteristics of DT has increased the utilization of DT in different scenarios, such as medical environments, healthcare, manufacturing industries, aerospace, etc. However, these utilizations have also brought serious security pitfalls in DT deployment. Towards this, several authentication protocols with different security and privacy features for DT environments have been proposed. In this article, we first review a recently proposed two-factor authentication protocol for DT environments that utilizes the blockchain technology. However, the analyzed scheme is unable to offer the desirable security and cannot withstand various security attacks like offline password-guessing attack, smart card stolen attack, anonymity property, and known session-specific temporary information attack. We also demonstrate that an attacker can impersonate the analyzed protocol’s legal user, owner, and cloud server. To mitigate these security loopholes, we devise an effective three-factor privacy-preserving authentication scheme for DT environments. The proposed work is demonstrated to be secure by performing the informal security analysis, the formal security analysis using the widely recognized Burrows-Abadi-Needham (BAN) logic, and the Real-or-Random (ROR) model. A detailed comparative study with the existing competing schemes including the analyzed scheme demonstrates that the devised framework furnishes better security features while also having lower computation costs and comparable communication costs than the existing schemes.

The key factor accelerating the development of intelligent transportation and reduced traffic congestion is the vehicle ad hoc network (VANET). However, the swift upsurge in the diversity of vehicles puts the development of the vehicle network's safety system through plentiful glitches. To receive the messages promptly, attain the confidentiality of vehicle identity, and authentication swiftly in VANET, we present a new identity-based aggregate signature (IBAGS) system using the lattices. The proposed lattice-based method presented in this article combines the batch verification and aggregate signature scheme (ASS), which finishes the authentication of several vehicular units and aggregates multiple users' signatures into a single (compact) signature. This boosts the competence of the verification process. Furthermore, the vehicle pseudo-identities are also utilized to meet the goals of vehicle identity and privacy protection. Finally, dependable cloud storage technology efficiently enables the secure storing of message signatures. This article demonstrates the lower storage overhead and increases authentication efficiency while maintaining security compared to similar competing schemes.