2-dimension (2D) materials have gained academic and industrial attention due to their high surface area-to-volume ratios, strong interatomic bonding, and good mechanical flexibility, making them suitable for a wide range of applications including energy storage, optoelectronics, and healthcare. An important step in realizing their potential is to build heterostructures by stacking 2D materials with thin […]

In the last twenty years, many efforts have been made to study the incredible properties that boron nitride nanotubes (BNNTs) and nanosheets (BNNSs) have presented since their discovery, especially through their excellent performance in the aerospace industry. In parallel, bio-based materials such as nanocellulose have been considered a cheap and attractive material for developing nanocomposites […]

in this project, we are exploring the combination of quantum computing with machine learning to ensure a quantum-enhanced machine learning. We are focusing on the improvement of the accuracy and reliability of machine learning algorithms. While quantum improvements in supervised, unsupervised learning, and reinforcement learning have been reported, distributed models of machine learning such as […]

The development of quantum technology is exciting frontier being pursued globally due to its potential for transformative innovations across information security, communications, medicine, energy and more. A critical component if the availability of robust quantum light sources. This research focuses on a promising approach – creating quantum light sources that can operate at room temperature. […]

The emergence of high-harmonic generation in solids has opened the door to countless new techniques for attosecond control in electronic devices. Simultaneously, quantum materials have become increasingly important for next-generation technologies that exploiting quantum mechanics for computation or sensing. Of particular interest is the family of 2D semiconductors, which are known to host an interesting […]

The rapid advancement in satellite technology has led to an exponential increase in the volume of satellite images. Analyzing the semantic content of these images is a challenging task due to the high dimensionality and complexity of the data. Traditional kernel methods, while effective, suffer from quadratic algorithmic complexity and significant time overhead in kernel […]

This project is investigation of conventional air vehicle localization using GPS and mobile networks and verification on that their weaknesses in the accurate and seamless localization can be supplemented or strengthened by future quantum sensor-based localization to discover a new blue ocean in the mobility industry.

Cryptographic algorithms are fundamental tools for securing digital information, with symmetric and asymmetric algorithms serving as the cornerstone of modern encryption techniques. Symmetric algorithms utilize a single key for both encryption and decryption, while asymmetric algorithms employ a pair of keys for these operations. Classical cryptographic algorithms, including RSA and AES, have been extensively utilized […]

The project involves the development of a machine learning model which can predict targeted properties of materials from their band structure. Computational modeling methods, such as ab initio (quantum chemistry) calculations, Density Functional Theory and Molecular Dynamics, enable us to calculate properties of materials from their crystalline structure. The band structure is a representation of […]

Most computers and materials work on scales such that quantum effects can be comfortably ignored. But as we aim to make computers ever smaller, quantum effects will cause difficulties; however, they also provide opportunities. Spin-crossover (SCO) materials are molecules that can “Flip” between two states: either high or low spin binary states, making them essentially […]

This project aims to simulate the melting of clusters that are important in the field of nanotechnology and catalysis. We will improve the computational efficiency of Parallel Tempering Monte Carlo (PTMC) simulations by integrating GPU capabilities and interfacing with Gaussian software for energy calculations. The project is an essential part of a Ph.D. thesis that […]

This Mitacs Globalink Research Award will support a research collaboration involving Imen Hemmedi, a PhD student working in the group of Dr. Nabila Bitri at the Ecole Nationale Supérieure d’Ingénieurs de Tunis and Prof. Jean-Michel Ménard at the University of Ottawa. The project focuses on leveraging the unique properties of quantum materials to explore innovative […]