Master’s Thesis. Studied quantum virtual cooling algorithms to improve quantum computation stability and efficiency. Utilized qubit entanglement and holographic error correction to access otherwise inaccessible phases while reducing physical costs.

Collaborator: Prof. Kristan Jensen

Developed secure and scalable protocols for quantum and post-quantum cryptography, leveraging advanced techniques like topological data analysis and combinatorics. Focused on enhancing security and efficiency in quantum communication and consensus protocols.

Collaborator: Prof. Gavin Brennen

Developed topological quantum neural networks to enhance deep learning generalization and quantum algorithm efficiency. Modeled information flow using topological quantum field theory, enabling scalable quantum computing tasks.

Supervisor: Prof. Antonino Marcianò

Explored how topological wormholes influence qubit entanglement and quantum network stability. Analyzed tunneling events and wormhole dynamics to improve quantum algorithms and architectures.

Supervisor: Prof. Igor Boettcher

Bachelor’s Thesis. Investigated the existence of de Sitter vacua in string theory and their role in modeling our universe. Analyzed quantum effects on compactified spaces to bypass theoretical constraints and enhance understanding of quantum gravity.

Supervisor: Prof. Keshav Dasgupta

Modeled cosmic string signals amidst non-linear noise from early-universe phase transitions. Utilized match-filtering techniques to identify signals and study their formation and evolution using quantum field theory.

Supervisor: Prof. Robert Brandenberger

Studied photon-recycling propulsion systems, focusing on quantum effects and efficient momentum transfer. Analyzed light interactions and radiation pressure to optimize energy transfer for quantum and aerospace technologies.

Supervisor: Prof. Andrew Higgins

Analyzed fast radio bursts signals to explore black-white hole tunneling connections. Processed CHIME data to correct for noise and characterize scintillation patterns, improving astrophysical signal interpretation.

Supervisor: Prof. Victoria Kaspi

Developed neural network models to optimize radiation therapies for tumors. Simulated helical trajectories to reduce damage to healthy tissues while enhancing targeting precision.

Supervisor: Prof. Marija Popovic

Studied γ-rays in supernovae and tidal disruption events using Fermi-LAT data. Cleaned background noise and performed statistical analyses to identify and characterize high-energy sources.

Supervisor: Prof. Kenneth Ragan

Solved quantum trajectories in pilot-wave theory using neural networks and the Crank-Nicolson method. Developed scripts to efficiently compute trajectories for diverse potentials in quantum systems.

Supervisor: Prof. Ivan Ivanov