http://hdl.handle.net/10453/148704 2026-07-17T01:00:55Z http://hdl.handle.net/10453/195688 Title: Dynamic Continuous Variable Quantum Key Distribution for Securing a Future Global Quantum Network Authors: Sayat, MT; Kish, SP; Lam, PK; Rattenbury, NJ; Cater, JE Abstract: Abstract Continuous variable quantum key distribution (CVQKD) is a developing method to secure information exchange in future quantum networks. With the recent developments in quantum technology and greater access to space, a global quantum network secured by CVQKD can be within reach. In this work, the structures of existing QKD networks are analyzed, and how they can be fit into a general overarching three‐layer QKD network architecture for the endeavor of a global QKD network. Such a network can comprise different links in fiber and free‐space. The finite size limit secret key rates (SKRs) with multidimensional reconciliation are calculated for the different links for which CVQKD can be used in such a network. The results show that CVQKD generally achieves longer distances and larger SKRs in inter‐satellite, satellite‐to‐ground, fiber, and underwater links in descending order. The different links and nodes are classified and secret key distribution is studied as a graph problem. The link capacity, a routing metric for secret key distribution, which considers a dynamic SKR based on dynamic links is presented. Its use in simulated CVQKD networks is presented for the aim of spatiotemporal secret key distribution through a dynamic CVQKD network. 2025-10-01T00:00:00Z http://hdl.handle.net/10453/195681 Title: Short-time simulation of quantum dynamics by Pauli measurements Authors: Faehrmann, PK; Eisert, J; Kieferová, M; Kueng, R Abstract: Simulating the dynamics of complex quantum systems is a central application of quantum devices. Here we propose leveraging the power of measurements to simulate short-time quantum dynamics of physically prepared quantum states in classical postprocessing using a truncated Taylor series approach. While limited to short simulation times, our hybrid quantum-classical method is equipped with rigorous error bounds. It is extendable to estimate low-order Taylor approximations of smooth time-dependent functions of tractable linear combinations of measurable operators. These insights can be made use of in the context of Hamiltonian learning and device verification, short-time imaginary-time evolution, or the application of intractable operations to subuniversal quantum simulators in classical postprocessing. 2025-07-01T00:00:00Z http://hdl.handle.net/10453/195639 Title: Enhancing E-Commerce Recommendation Systems with Sentiment Analysis Authors: Shili, M; Chniti, H; Sohaib, O; Aleidi, A Abstract: Consumer reviews significantly influence purchasing decisions on e-commerce platforms and social media. Many e-commerce sites utilize recommendation systems to address information overload, which helps users decide on products. Integrating user opinions into these systems is a burgeoning research area, highlighting the importance of sentiment analysis and opinion classification. This study explores enhancing recommendation systems by analyzing product review sentiments to tailor suggestions to consumer profiles. Using natural language processing (NLP), we examine the sentiment of review texts to determine their positivity or negativity. Logistic regression and Naïve Bayes algorithms assign scores to these sentiments. These polarity scores feed into a collaborative item–item filtering system during the recommendation phase, resulting in an innovative and efficient recommendation system. Our study employed Naïve Bayes, logistic regression, and K-nearest neighbors (KNN) algorithms. Our approach, tested on the Amazon database, demonstrated high-quality recommendations with an impressive accuracy rate of 94% and an error rate of 6%. Our findings revealed that logistic regression provided better precision than Naïve Bayes when the dataset was used for training and testing. This underscores the potential of combining sentiment analysis with recommendation systems to enhance consumer e-commerce experiences. 2026-01-01T00:00:00Z http://hdl.handle.net/10453/195638 Title: The Impact of Personal Data Collection on Consumer Autonomy: A Study in the UAE Authors: Sohaib, O; Arman, A; Begum, V; Bhatti, T Abstract: In the era of Big Data, businesses leverage advanced data collection techniques to enhance their intelligence and decision-making capabilities. Despite these benefits, online data collection practices can generate significant tensions among consumers, particularly regarding their sense of autonomy. This study, focusing on the United Arab Emirates (UAE), explores the relationship between personal online data collection and consumer autonomy. We examine the impact of consumer acceptability of data collection, consumer attitudes, and consumer consent of data practices on consumer autonomy. This study aims to present these findings and discuss the broader implications for businesses and policymakers. This research is essential for developing strategies that protect consumer interests while allowing businesses to benefit from Big Data analytics. 2026-01-01T00:00:00Z