Implemented iterative phase reconstruction algorithms to generate spot arrays using a phase spatial light modulators, reaching 92% simulated power efficiency.

Investigating alternative neural network phase reconstruction algorithms for improved power efficiency and trap depth.

Extending algorithm to 3D trap arrays through wavefront propagation techniques.

Designed transformer and structured selective state space models to decode the rotated planar code, a type of quantum error correction code.

Implemented and trained the models using PyTorch to decode low distance rotated planar codes.

Scaled decoders to higher distance codes using state compression techniques.

Trained a federated QCNN using the Qiskit Machine Learning library, achieving 89% simulator test accuracy and 70% IBM QPU test accuracy on the MNIST dataset.

Implemented differential privacy to obfuscate sensitive client data, and performed a hyperparameter search to find an appropriate level of privacy.

Contributed to a white paper detailing relevant literature and proposed approaches on the weapon target assignment problem, which resulted in the team's acceptance to the competition.

Played a leading role in brainstorming and implementing approaches for automatic scheduling and coordination of advanced weapon systems.

Architected, implemented, and trained several approaches to reduce damage to high value assets, including a Deep Q-Learning agent and heuristic-driven Greedy agent.

The W&M team won 3rd place and $20,000 in prize money.

Approximated arbitrary quantum operators using the Pauli product rotations, exponentiated elements of the Pauli group.

Transformed problem into nonconvex positive semidefinite programming problem, and optimized using a trust-region approach.