Ph.D. Student in Physics
Clemson University — Clemson Quantum Group
I study quantum computing, topological condensed matter, and quantum machine learning. My research focuses on using variational quantum algorithms to identify topological superconductor candidates and developing ML pipelines for quantum system characterization.
I am a physics Ph.D. student at Clemson University working in the Clemson Quantum Group. My research lies at the intersection of quantum computing, condensed matter theory, and machine learning.
On the physics side, I develop variational quantum eigensolver (VQE) frameworks to study topological superconductors via the Kitaev chain model, probing Majorana zero modes and topological phase transitions. On the ML side, I build convolutional neural network pipelines to predict Majorana zero mode visibility from nanowire conductance data and apply quantum kernels to real-world classification problems.
I am also active in the quantum computing hackathon community, with projects spanning quantum error correction, cat qubit simulation, and hybrid quantum-classical healthcare applications.
Core areas of my current work
Variational quantum eigensolver (VQE) framework for identifying topological superconductor candidates. Studying Majorana zero modes, winding numbers, and topological phase transitions in the Kitaev chain and related models.
CNN pipelines for predicting Majorana zero mode visibility from nanowire conductance data. Quantum support vector machines and quantum neural networks for classification tasks including tornado prediction and EEG analysis.
Reed-Muller decoding-based Clifford+T circuit synthesis and optimization. Cat qubit simulation for open quantum systems. Quantum error correction and fault-tolerant computation research.