Pirmatov Abdymanap Ziiaidinovich

Background: Machine learning (ML) has developed significantly over the years, changing several industries through the use of automation and Big Data. By building better next-generation machine learning models, AI’s future has the potential of improving on existing problematic methods such as scalability, interpretability, and generalization. Objective: This article examines about how new generation of ML models are developed and used to explain about the capabilities of AI in different fields. In particular, it is focused on changes in structural models, certain methods of training them, and the application of brand-new technologies as quantum computing. Methods: A review of the state of the art and several case studies were carried out with regard to the latest work being done on different types of ML algorithms such as transformer models, reinforcement learning, and Neural Architecture Search. Moreover, the given models were tested in experiments concerning the applicability of these models in tasks including image recognition, natural language processing, and in autonomous systems. Results: The next-gen models, thereby outperformed the traditional models in terms of accuracy, computational speed, and flexibility. The identified benefits were decreased training time, better interpretability, and better performance with multi-modal and cross-domain tasks. Conclusion: These new generation of ML models are the game changers in AI development solving previous challenges while providing opportunities across numerous sectors. In this vein, further research in this field is needed to achieve AI’s solving of problems.

Background: Quantum Key Distribution (QKD) has turned into a crucial point for secure communication in the era of quantum networks. Quantum key distribution provides the client with a theoretically secure key by taking advantage of the principles of quantum mechanics to counteract what could be posed by quantum computing to classical cryptography. Photons are lost in the system and there are some limitations which don’t allow scalability and integration with already existing networks. Objective: The study seeks to assess the viability of QKD systems, review some of the challenges associated with it, and investigate possible methods of utilizing both QKD and PQC to cope with new security threats in telecommunication industry. Methods: An in-depth analysis was made based on the experimental observations of key generation rates, photon loss, error correction, data throughput, and latency. Performance of quantum repeaters was experimented with for the purposes of measuring distance improvement abilities. A combined QKD-PQC approach was assessed for integrated integration for restricted settings. Results: QKD was seen to have high security and high performance in short distances and when quantum repeaters were implemented the distance could be greatly enhanced. In the QKD-PQC model, the rate of error correction, throughput, and scalability was noticed to be higher than in standalone QKD. Challenges that faced the work were photon loss, processing latency, and system vulnerabilities. Conclusion: New opportunities for secure communication are opened with QKD supported by quantum repeaters and hybrid cryptographic approaches. The technical and operational issues need to be resolved to realize the potential role of B3G evolution in enabling global telecommunications for the mass market.